aldur/chat-log.txt Secret

## chat-log.txt
bash-5.3$ ./sandbox.sh llm chat
Chatting with llama-server
Type 'exit' or 'quit' to exit
Type '!multi' to enter multiple lines, then '!end' to finish
Type '!edit' to open your default editor and modify the prompt
Type '!fragment <my_fragment> [<another_fragment> ...]' to insert one or more fragments
> !edit
Here is a detailed proofread of your blog post. Overall, it's a solid technical piece with a clear value proposition. However, there are several grammatical errors, numbering inconsistencies, and a few technical phrasing issues that should be addressed to make it polished.

### 🔴 Critical Errors & Inconsistencies

1.  **Model Name Consistency:**
    *   **Issue:** You refer to the model as **Qwen3.5** in the title, excerpt, and intro, but switch to **Qwen3** in the Conclusion.
    *   **Fix:** Standardize on `Qwen3.5` throughout the post (or `Qwen 3.5` for readability).
    *   **Location:** Conclusion ("On a MacBook Pro... Qwen3 `35B-A3B`...").

2.  **List Numbering:**
    *   **Issue:** In the "Sandboxing" and "Local clients" sections, your Markdown lists use `1.` for every item, rendering as `1, 1, 1`.
    *   **Fix:** Change the second and third items in these lists to `2.` and `3.`.
    *   **Location:** "Sandboxing" section (list of 3 items) and "Local clients" section (list of 2 items).

3.  **HTTP Protocol Typo:**
    *   **Issue:** In the QEMU VM code block, the curl command is missing a slash in the protocol.
    *   **Fix:** Change `http:/10.0.2.2` to `http://10.0.2.2`.
    *   **Location:** "In a QEMU VM" code block.

4.  **Technical Inaccuracy (Bytecode):**
    *   **Issue:** You mention allowing access to "executable bytecode". On macOS (and Linux), native executables are typically Mach-O or ELF binaries, not "bytecode" (which implies Java, Python, etc.).
    *   **Fix:** Change to "the executable binary" or just "the executable".
    *   **Location:** "Server" section ("...allows access to the executable bytecode...").

### 🟠 Grammar & Syntax

1.  **Verb Agreement:**
    *   **Original:** "The `sandbox.sh` scripts automatically **takes** care..."
    *   **Correction:** "The `sandbox.sh` scripts automatically **take** care..." (Plural subject "scripts" needs plural verb).
    *   **Original:** "Now that the big AI companies **using** it..."
    *   **Correction:** "Now that the big AI companies **are using** it..."

2.  **Spelling & Word Choice:**
    *   **Original:** "...has **lead** to [code execution vulnerabilities]..."
    *   **Correction:** "...has **led** to..." (Past participle of lead).
    *   **Original:** "...guarantees both integrity and **confidentially**..."
    *   **Correction:** "...guarantees both integrity and **confidentiality**..." (Noun form needed).
    *   **Original:** "...64GB of **RAMs**..."
    *   **Correction:** "...64GB of **RAM**..." (RAM is uncountable).
    *   **Original:** "...these relatively small **model** are able..."
    *   **Correction:** "...these relatively small **models** are able..."
    *   **Original:** "...preventing `opencode` **to leak** your prompts..."
    *   **Correction:** "...preventing `opencode` **from leaking** your prompts..."

3.  **Sentence Structure:**
    *   **Original:** "...so `llama-server` from the host **available** at `10.0.2.2:8080`..."
    *   **Correction:** "...so `llama-server` from the host **is available** at `10.0.2.2:8080`..." (Missing verb).
    *   **Original:** "YOLO **modo**"
    *   **Correction:** "YOLO **mode**" (Unless you specifically want the Italian/Spanish flair, "mode" is the standard English term for this context).

### 🟡 Flow & Clarity

1.  **Excerpt Clarity:**
    *   **Current:** "...plus local Qwen3.5 from a QEMU VM."
    *   **Critique:** This implies the model runs *inside* the VM. Your post explains the model runs on the host and is accessed *by* the VM.
    *   **Suggestion:** "...plus local Qwen3.5 **accessed via** a QEMU VM."

2.  **Confusing Parenthetical:**
    *   **Current:** "...preventing `opencode` to leak your prompts (that's how it gives a name to sessions)."
    *   **Critique:** This parenthetical is confusing. Does `opencode` name sessions based on the prompts? If the sandbox prevents leaking, how does it name them?
    *   **Suggestion:** Clarify or remove. If the point is that the sandbox stops the prompts from leaving the machine, the session naming part might be distracting.

3.  **The "Intel CPUs" Non-Sequitur:**
    *   **Current:** "Plus, their OCR capabilities are impressive, even on Intel CPUs!"
    *   **Critique:** You are testing on an M3 Max MacBook. Mentioning Intel CPUs here feels like a non-sequitur or a typo. Did you test it elsewhere?
    *   **Suggestion:** If you haven't tested on Intel, remove the reference to avoid confusing readers. If you mean the model *architecture* supports Intel, rephrase: "...impressive across different architectures, including Intel CPUs."

4.  **Code Block Artifact:**
    *   **Current:** `{"status":"ok"}⏎`
    *   **Critique:** The `⏎` symbol looks like an artifact from a terminal copy-paste.
    *   **Suggestion:** Remove the symbol to keep the code block clean.

5.  **Footnote Link:**
    *   **Current:** `manpath=macOS+26.3` (Footnote 3)
    *   **Critique:** macOS version numbers are currently around 15 (Sequoia). `26.3` looks suspicious (possibly a FreeBSD version number mixed in?).
    *   **Suggestion:** Verify the link or remove the version number if it's dynamic.

### 🟢 Specific Edits (Before/After)

**Section: Sandboxing**
> *Before:* "when they can wreak havoc in YOLO modo (`--dangerously-skip-permissions`)."
> *After:* "when they can wreak havoc in YOLO mode (`--dangerously-skip-permissions`)."

> *Before:* "since parsing the GGUF files representing the models has lead to [code execution vulnerabilities][10]";"
> *After:* "since parsing GGUF files has led to [code execution vulnerabilities][10]";"

**Section: Local clients**
> *Before:* "The `sandbox.sh` scripts automatically takes care of the configuration files..."
> *After:* "The `sandbox.sh` script automatically takes care of the configuration files..." (Or "scripts... take").

**Section: In a QEMU VM**
> *Before:* "so `llama-server` from the host available at `10.0.2.2:8080`:"
> *After:* "so `llama-server` from the host is available at `10.0.2.2:8080`:"

**Section: Conclusion**
> *Before:* "On a MacBook Pro with an M3 Max and 64GB of RAMs, Qwen3 `35B-A3B` is quick enough..."
> *After:* "On a MacBook Pro with an M3 Max and 64GB of RAM, Qwen3.5 `35B-A3B` is quick enough..."

> *Before:* "Unsurprisingly, the quality of results is lower than frontier hosted models, but it's a big step forward: these relatively small model are able to make small, interactive changes."
> *After:* "Unsurprisingly, the quality of results is lower than frontier hosted models, but it's a big step forward: these relatively small models are capable of making small, interactive edits."

### Summary
The content is excellent and the "default-deny" approach is a great hook. Once you fix the grammar, list numbering, and model name consistency, this will be a very high-quality technical post. Good luck!

## llama-server-logs.txt
srv  params_from_: Chat format: peg-native
slot get_availabl: id  3 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id  3 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist
slot launch_slot_: id  3 | task 0 | processing task, is_child = 0
slot update_slots: id  3 | task 0 | new prompt, n_ctx_slot = 262144, n_keep = 0, task.n_tokens = 11
slot update_slots: id  3 | task 0 | n_tokens = 0, memory_seq_rm [0, end)
slot init_sampler: id  3 | task 0 | init sampler, took 0.00 ms, tokens: text = 11, total = 11
slot update_slots: id  3 | task 0 | prompt processing done, n_tokens = 11, batch.n_tokens = 11
srv  log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
slot print_timing: id  3 | task 0 |
prompt eval time =    1229.25 ms /    11 tokens (  111.75 ms per token,     8.95 tokens per second)
       eval time =   18463.71 ms /   194 tokens (   95.17 ms per token,    10.51 tokens per second)
      total time =   19692.96 ms /   205 tokens
slot      release: id  3 | task 0 | stop processing: n_tokens = 204, truncated = 0
srv  update_slots: all slots are idle
srv  params_from_: Chat format: peg-native
slot get_availabl: id  2 | task -1 | selected slot by LRU, t_last = -1
slot launch_slot_: id  2 | task -1 | sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist
slot launch_slot_: id  2 | task 195 | processing task, is_child = 0
slot update_slots: id  2 | task 195 | new prompt, n_ctx_slot = 262144, n_keep = 0, task.n_tokens = 2573
slot update_slots: id  2 | task 195 | n_tokens = 0, memory_seq_rm [0, end)
slot update_slots: id  2 | task 195 | prompt processing progress, n_tokens = 2048, batch.n_tokens = 2048, progress = 0.795958
slot update_slots: id  2 | task 195 | n_tokens = 2048, memory_seq_rm [2048, end)
slot update_slots: id  2 | task 195 | prompt processing progress, n_tokens = 2061, batch.n_tokens = 13, progress = 0.801010
slot update_slots: id  2 | task 195 | n_tokens = 2061, memory_seq_rm [2061, end)
slot init_sampler: id  2 | task 195 | init sampler, took 0.23 ms, tokens: text = 2573, total = 2573
slot update_slots: id  2 | task 195 | prompt processing done, n_tokens = 2573, batch.n_tokens = 512
slot update_slots: id  2 | task 195 | created context checkpoint 1 of 32 (pos_min = 2060, pos_max = 2060, n_tokens = 2061, size = 149.626 MiB)
srv  log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
slot print_timing: id  2 | task 195 |
prompt eval time =   14795.45 ms /  2573 tokens (    5.75 ms per token,   173.90 tokens per second)
       eval time =  688980.45 ms /  5453 tokens (  126.35 ms per token,     7.91 tokens per second)
      total time =  703775.90 ms /  8026 tokens
slot      release: id  2 | task 195 | stop processing: n_tokens = 8025, truncated = 0
srv  update_slots: all slots are idle

## model-info.txt
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv   0:                       general.architecture str              = qwen35
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                     general.sampling.top_k i32              = 20
llama_model_loader: - kv   3:                     general.sampling.top_p f32              = 0.950000
llama_model_loader: - kv   4:                      general.sampling.temp f32              = 0.600000
llama_model_loader: - kv   5:                               general.name str              = Qwen3.5-27B
llama_model_loader: - kv   6:                           general.basename str              = Qwen3.5-27B
llama_model_loader: - kv   7:                       general.quantized_by str              = Unsloth
llama_model_loader: - kv   8:                         general.size_label str              = 27B
llama_model_loader: - kv   9:                            general.license str              = apache-2.0
llama_model_loader: - kv  10:                       general.license.link str              = https://huggingface.co/Qwen/Qwen3.5-2...
llama_model_loader: - kv  11:                           general.repo_url str              = https://huggingface.co/unsloth
llama_model_loader: - kv  12:                   general.base_model.count u32              = 1
llama_model_loader: - kv  13:                  general.base_model.0.name str              = Qwen3.5 27B
llama_model_loader: - kv  14:          general.base_model.0.organization str              = Qwen
llama_model_loader: - kv  15:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen3.5-27B
llama_model_loader: - kv  16:                               general.tags arr[str,3]       = ["qwen3_5_moe", "unsloth", "image-tex...
llama_model_loader: - kv  17:                         qwen35.block_count u32              = 64
llama_model_loader: - kv  18:                      qwen35.context_length u32              = 262144
llama_model_loader: - kv  19:                    qwen35.embedding_length u32              = 5120
llama_model_loader: - kv  20:                 qwen35.feed_forward_length u32              = 17408
llama_model_loader: - kv  21:                qwen35.attention.head_count u32              = 24
llama_model_loader: - kv  22:             qwen35.attention.head_count_kv u32              = 4
llama_model_loader: - kv  23:             qwen35.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
llama_model_loader: - kv  24:                      qwen35.rope.freq_base f32              = 10000000.000000
llama_model_loader: - kv  25:    qwen35.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  26:                qwen35.attention.key_length u32              = 256
llama_model_loader: - kv  27:              qwen35.attention.value_length u32              = 256
llama_model_loader: - kv  28:                     qwen35.ssm.conv_kernel u32              = 4
llama_model_loader: - kv  29:                      qwen35.ssm.state_size u32              = 128
llama_model_loader: - kv  30:                     qwen35.ssm.group_count u32              = 16
llama_model_loader: - kv  31:                  qwen35.ssm.time_step_rank u32              = 48
llama_model_loader: - kv  32:                      qwen35.ssm.inner_size u32              = 6144
llama_model_loader: - kv  33:             qwen35.full_attention_interval u32              = 4
llama_model_loader: - kv  34:                qwen35.rope.dimension_count u32              = 64
llama_model_loader: - kv  35:                       tokenizer.ggml.model str              = gpt2
llama_model_loader: - kv  36:                         tokenizer.ggml.pre str              = qwen35
llama_model_loader: - kv  37:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv  38:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv  39:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv  40:                tokenizer.ggml.eos_token_id u32              = 248046
llama_model_loader: - kv  41:            tokenizer.ggml.padding_token_id u32              = 248055
llama_model_loader: - kv  42:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
llama_model_loader: - kv  43:               general.quantization_version u32              = 2
llama_model_loader: - kv  44:                          general.file_type u32              = 7
llama_model_loader: - kv  45:                      quantize.imatrix.file str              = Qwen3.5-27B-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv  46:                   quantize.imatrix.dataset str              = unsloth_calibration_Qwen3.5-27B.txt
llama_model_loader: - kv  47:             quantize.imatrix.entries_count u32              = 496
llama_model_loader: - kv  48:              quantize.imatrix.chunks_count u32              = 80
llama_model_loader: - type  f32:  353 tensors
llama_model_loader: - type q8_0:  498 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q8_0
print_info: file size   = 26.62 GiB (8.50 BPW)
load: 0 unused tokens
load: printing all EOG tokens:
load:   - 248044 ('<|endoftext|>')
load:   - 248046 ('<|im_end|>')
load:   - 248063 ('<|fim_pad|>')
load:   - 248064 ('<|repo_name|>')
load:   - 248065 ('<|file_sep|>')
load: special tokens cache size = 33
load: token to piece cache size = 1.7581 MB
print_info: arch                  = qwen35
print_info: vocab_only            = 0
print_info: no_alloc              = 0
print_info: n_ctx_train           = 262144
print_info: n_embd                = 5120
print_info: n_embd_inp            = 5120
print_info: n_layer               = 64
print_info: n_head                = 24
print_info: n_head_kv             = 4
print_info: n_rot                 = 64
print_info: n_swa                 = 0
print_info: is_swa_any            = 0
print_info: n_embd_head_k         = 256
print_info: n_embd_head_v         = 256
print_info: n_gqa                 = 6
print_info: n_embd_k_gqa          = 1024
print_info: n_embd_v_gqa          = 1024
print_info: f_norm_eps            = 0.0e+00
print_info: f_norm_rms_eps        = 1.0e-06
print_info: f_clamp_kqv           = 0.0e+00
print_info: f_max_alibi_bias      = 0.0e+00
print_info: f_logit_scale         = 0.0e+00
print_info: f_attn_scale          = 0.0e+00
print_info: n_ff                  = 17408
print_info: n_expert              = 0
print_info: n_expert_used         = 0
print_info: n_expert_groups       = 0
print_info: n_group_used          = 0
print_info: causal attn           = 1
print_info: pooling type          = 0
print_info: rope type             = 40
print_info: rope scaling          = linear
print_info: freq_base_train       = 10000000.0
print_info: freq_scale_train      = 1
print_info: n_ctx_orig_yarn       = 262144
print_info: rope_yarn_log_mul     = 0.0000
print_info: rope_finetuned        = unknown
print_info: mrope sections        = [11, 11, 10, 0]
print_info: ssm_d_conv            = 4
print_info: ssm_d_inner           = 6144
print_info: ssm_d_state           = 128
print_info: ssm_dt_rank           = 48
print_info: ssm_n_group           = 16
print_info: ssm_dt_b_c_rms        = 0
print_info: model type            = 27B
print_info: model params          = 26.90 B
print_info: general.name          = Qwen3.5-27B
print_info: vocab type            = BPE
print_info: n_vocab               = 248320
print_info: n_merges              = 247587
print_info: BOS token             = 11 ','
print_info: EOS token             = 248046 '<|im_end|>'
print_info: EOT token             = 248046 '<|im_end|>'
print_info: PAD token             = 248055 '<|vision_pad|>'
print_info: LF token              = 198 'Ċ'
print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
print_info: FIM MID token         = 248061 '<|fim_middle|>'
print_info: FIM PAD token         = 248063 '<|fim_pad|>'
print_info: FIM REP token         = 248064 '<|repo_name|>'
print_info: FIM SEP token         = 248065 '<|file_sep|>'
print_info: EOG token             = 248044 '<|endoftext|>'
print_info: EOG token             = 248046 '<|im_end|>'
print_info: EOG token             = 248063 '<|fim_pad|>'
print_info: EOG token             = 248064 '<|repo_name|>'
print_info: EOG token             = 248065 '<|file_sep|>'
print_info: max token length      = 256
	bash-5.3$ ./sandbox.sh llm chat
	Chatting with llama-server
	Type 'exit' or 'quit' to exit
	Type '!multi' to enter multiple lines, then '!end' to finish
	Type '!edit' to open your default editor and modify the prompt
	Type '!fragment <my_fragment> [<another_fragment> ...]' to insert one or more fragments
	> !edit
	Here is a detailed proofread of your blog post. Overall, it's a solid technical piece with a clear value proposition. However, there are several grammatical errors, numbering inconsistencies, and a few technical phrasing issues that should be addressed to make it polished.

	### 🔴 Critical Errors & Inconsistencies

	1. Model Name Consistency:
	* Issue: You refer to the model as Qwen3.5 in the title, excerpt, and intro, but switch to Qwen3 in the Conclusion.
	* Fix: Standardize on `Qwen3.5` throughout the post (or `Qwen 3.5` for readability).
	* Location: Conclusion ("On a MacBook Pro... Qwen3 `35B-A3B`...").

	2. List Numbering:
	* Issue: In the "Sandboxing" and "Local clients" sections, your Markdown lists use `1.` for every item, rendering as `1, 1, 1`.
	* Fix: Change the second and third items in these lists to `2.` and `3.`.
	* Location: "Sandboxing" section (list of 3 items) and "Local clients" section (list of 2 items).

	3. HTTP Protocol Typo:
	* Issue: In the QEMU VM code block, the curl command is missing a slash in the protocol.
	* Fix: Change `http:/10.0.2.2` to `http://10.0.2.2`.
	* Location: "In a QEMU VM" code block.

	4. Technical Inaccuracy (Bytecode):
	* Issue: You mention allowing access to "executable bytecode". On macOS (and Linux), native executables are typically Mach-O or ELF binaries, not "bytecode" (which implies Java, Python, etc.).
	* Fix: Change to "the executable binary" or just "the executable".
	* Location: "Server" section ("...allows access to the executable bytecode...").

	### 🟠 Grammar & Syntax

	1. Verb Agreement:
	* Original: "The `sandbox.sh` scripts automatically takes care..."
	* Correction: "The `sandbox.sh` scripts automatically take care..." (Plural subject "scripts" needs plural verb).
	* Original: "Now that the big AI companies using it..."
	* Correction: "Now that the big AI companies are using it..."

	2. Spelling & Word Choice:
	* Original: "...has lead to [code execution vulnerabilities]..."
	* Correction: "...has led to..." (Past participle of lead).
	* Original: "...guarantees both integrity and confidentially..."
	* Correction: "...guarantees both integrity and confidentiality..." (Noun form needed).
	* Original: "...64GB of RAMs..."
	* Correction: "...64GB of RAM..." (RAM is uncountable).
	* Original: "...these relatively small model are able..."
	* Correction: "...these relatively small models are able..."
	* Original: "...preventing `opencode` to leak your prompts..."
	* Correction: "...preventing `opencode` from leaking your prompts..."

	3. Sentence Structure:
	* Original: "...so `llama-server` from the host available at `10.0.2.2:8080`..."
	* Correction: "...so `llama-server` from the host is available at `10.0.2.2:8080`..." (Missing verb).
	* Original: "YOLO modo"
	* Correction: "YOLO mode" (Unless you specifically want the Italian/Spanish flair, "mode" is the standard English term for this context).

	### 🟡 Flow & Clarity

	1. Excerpt Clarity:
	* Current: "...plus local Qwen3.5 from a QEMU VM."
	* Critique: This implies the model runs inside the VM. Your post explains the model runs on the host and is accessed by the VM.
	* Suggestion: "...plus local Qwen3.5 accessed via a QEMU VM."

	2. Confusing Parenthetical:
	* Current: "...preventing `opencode` to leak your prompts (that's how it gives a name to sessions)."
	* Critique: This parenthetical is confusing. Does `opencode` name sessions based on the prompts? If the sandbox prevents leaking, how does it name them?
	* Suggestion: Clarify or remove. If the point is that the sandbox stops the prompts from leaving the machine, the session naming part might be distracting.

	3. The "Intel CPUs" Non-Sequitur:
	* Current: "Plus, their OCR capabilities are impressive, even on Intel CPUs!"
	* Critique: You are testing on an M3 Max MacBook. Mentioning Intel CPUs here feels like a non-sequitur or a typo. Did you test it elsewhere?
	* Suggestion: If you haven't tested on Intel, remove the reference to avoid confusing readers. If you mean the model architecture supports Intel, rephrase: "...impressive across different architectures, including Intel CPUs."

	4. Code Block Artifact:
	* Current: `{"status":"ok"}⏎`
	* Critique: The `⏎` symbol looks like an artifact from a terminal copy-paste.
	* Suggestion: Remove the symbol to keep the code block clean.

	5. Footnote Link:
	* Current: `manpath=macOS+26.3` (Footnote 3)
	* Critique: macOS version numbers are currently around 15 (Sequoia). `26.3` looks suspicious (possibly a FreeBSD version number mixed in?).
	* Suggestion: Verify the link or remove the version number if it's dynamic.

	### 🟢 Specific Edits (Before/After)

	Section: Sandboxing
	> Before: "when they can wreak havoc in YOLO modo (`--dangerously-skip-permissions`)."
	> After: "when they can wreak havoc in YOLO mode (`--dangerously-skip-permissions`)."

	> Before: "since parsing the GGUF files representing the models has lead to [code execution vulnerabilities][10]";"
	> After: "since parsing GGUF files has led to [code execution vulnerabilities][10]";"

	Section: Local clients
	> Before: "The `sandbox.sh` scripts automatically takes care of the configuration files..."
	> After: "The `sandbox.sh` script automatically takes care of the configuration files..." (Or "scripts... take").

	Section: In a QEMU VM
	> Before: "so `llama-server` from the host available at `10.0.2.2:8080`:"
	> After: "so `llama-server` from the host is available at `10.0.2.2:8080`:"

	Section: Conclusion
	> Before: "On a MacBook Pro with an M3 Max and 64GB of RAMs, Qwen3 `35B-A3B` is quick enough..."
	> After: "On a MacBook Pro with an M3 Max and 64GB of RAM, Qwen3.5 `35B-A3B` is quick enough..."

	> Before: "Unsurprisingly, the quality of results is lower than frontier hosted models, but it's a big step forward: these relatively small model are able to make small, interactive changes."
	> After: "Unsurprisingly, the quality of results is lower than frontier hosted models, but it's a big step forward: these relatively small models are capable of making small, interactive edits."

	### Summary
	The content is excellent and the "default-deny" approach is a great hook. Once you fix the grammar, list numbering, and model name consistency, this will be a very high-quality technical post. Good luck!
	srv params_from_: Chat format: peg-native
	slot get_availabl: id 3 \| task -1 \| selected slot by LRU, t_last = -1
	slot launch_slot_: id 3 \| task -1 \| sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist
	slot launch_slot_: id 3 \| task 0 \| processing task, is_child = 0
	slot update_slots: id 3 \| task 0 \| new prompt, n_ctx_slot = 262144, n_keep = 0, task.n_tokens = 11
	slot update_slots: id 3 \| task 0 \| n_tokens = 0, memory_seq_rm [0, end)
	slot init_sampler: id 3 \| task 0 \| init sampler, took 0.00 ms, tokens: text = 11, total = 11
	slot update_slots: id 3 \| task 0 \| prompt processing done, n_tokens = 11, batch.n_tokens = 11
	srv log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
	slot print_timing: id 3 \| task 0 \|
	prompt eval time = 1229.25 ms / 11 tokens ( 111.75 ms per token, 8.95 tokens per second)
	eval time = 18463.71 ms / 194 tokens ( 95.17 ms per token, 10.51 tokens per second)
	total time = 19692.96 ms / 205 tokens
	slot release: id 3 \| task 0 \| stop processing: n_tokens = 204, truncated = 0
	srv update_slots: all slots are idle
	srv params_from_: Chat format: peg-native
	slot get_availabl: id 2 \| task -1 \| selected slot by LRU, t_last = -1
	slot launch_slot_: id 2 \| task -1 \| sampler chain: logits -> ?penalties -> ?dry -> ?top-n-sigma -> top-k -> ?typical -> top-p -> min-p -> ?xtc -> temp-ext -> dist
	slot launch_slot_: id 2 \| task 195 \| processing task, is_child = 0
	slot update_slots: id 2 \| task 195 \| new prompt, n_ctx_slot = 262144, n_keep = 0, task.n_tokens = 2573
	slot update_slots: id 2 \| task 195 \| n_tokens = 0, memory_seq_rm [0, end)
	slot update_slots: id 2 \| task 195 \| prompt processing progress, n_tokens = 2048, batch.n_tokens = 2048, progress = 0.795958
	slot update_slots: id 2 \| task 195 \| n_tokens = 2048, memory_seq_rm [2048, end)
	slot update_slots: id 2 \| task 195 \| prompt processing progress, n_tokens = 2061, batch.n_tokens = 13, progress = 0.801010
	slot update_slots: id 2 \| task 195 \| n_tokens = 2061, memory_seq_rm [2061, end)
	slot init_sampler: id 2 \| task 195 \| init sampler, took 0.23 ms, tokens: text = 2573, total = 2573
	slot update_slots: id 2 \| task 195 \| prompt processing done, n_tokens = 2573, batch.n_tokens = 512
	slot update_slots: id 2 \| task 195 \| created context checkpoint 1 of 32 (pos_min = 2060, pos_max = 2060, n_tokens = 2061, size = 149.626 MiB)
	srv log_server_r: done request: POST /v1/chat/completions 127.0.0.1 200
	slot print_timing: id 2 \| task 195 \|
	prompt eval time = 14795.45 ms / 2573 tokens ( 5.75 ms per token, 173.90 tokens per second)
	eval time = 688980.45 ms / 5453 tokens ( 126.35 ms per token, 7.91 tokens per second)
	total time = 703775.90 ms / 8026 tokens
	slot release: id 2 \| task 195 \| stop processing: n_tokens = 8025, truncated = 0
	srv update_slots: all slots are idle
	llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
	llama_model_loader: - kv 0: general.architecture str = qwen35
	llama_model_loader: - kv 1: general.type str = model
	llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
	llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
	llama_model_loader: - kv 4: general.sampling.temp f32 = 0.600000
	llama_model_loader: - kv 5: general.name str = Qwen3.5-27B
	llama_model_loader: - kv 6: general.basename str = Qwen3.5-27B
	llama_model_loader: - kv 7: general.quantized_by str = Unsloth
	llama_model_loader: - kv 8: general.size_label str = 27B
	llama_model_loader: - kv 9: general.license str = apache-2.0
	llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.5-2...
	llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
	llama_model_loader: - kv 12: general.base_model.count u32 = 1
	llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.5 27B
	llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
	llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.5-27B
	llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "unsloth", "image-tex...
	llama_model_loader: - kv 17: qwen35.block_count u32 = 64
	llama_model_loader: - kv 18: qwen35.context_length u32 = 262144
	llama_model_loader: - kv 19: qwen35.embedding_length u32 = 5120
	llama_model_loader: - kv 20: qwen35.feed_forward_length u32 = 17408
	llama_model_loader: - kv 21: qwen35.attention.head_count u32 = 24
	llama_model_loader: - kv 22: qwen35.attention.head_count_kv u32 = 4
	llama_model_loader: - kv 23: qwen35.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
	llama_model_loader: - kv 24: qwen35.rope.freq_base f32 = 10000000.000000
	llama_model_loader: - kv 25: qwen35.attention.layer_norm_rms_epsilon f32 = 0.000001
	llama_model_loader: - kv 26: qwen35.attention.key_length u32 = 256
	llama_model_loader: - kv 27: qwen35.attention.value_length u32 = 256
	llama_model_loader: - kv 28: qwen35.ssm.conv_kernel u32 = 4
	llama_model_loader: - kv 29: qwen35.ssm.state_size u32 = 128
	llama_model_loader: - kv 30: qwen35.ssm.group_count u32 = 16
	llama_model_loader: - kv 31: qwen35.ssm.time_step_rank u32 = 48
	llama_model_loader: - kv 32: qwen35.ssm.inner_size u32 = 6144
	llama_model_loader: - kv 33: qwen35.full_attention_interval u32 = 4
	llama_model_loader: - kv 34: qwen35.rope.dimension_count u32 = 64
	llama_model_loader: - kv 35: tokenizer.ggml.model str = gpt2
	llama_model_loader: - kv 36: tokenizer.ggml.pre str = qwen35
	llama_model_loader: - kv 37: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
	llama_model_loader: - kv 38: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
	llama_model_loader: - kv 39: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
	llama_model_loader: - kv 40: tokenizer.ggml.eos_token_id u32 = 248046
	llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 248055
	llama_model_loader: - kv 42: tokenizer.chat_template str = {%- set image_count = namespace(value...
	llama_model_loader: - kv 43: general.quantization_version u32 = 2
	llama_model_loader: - kv 44: general.file_type u32 = 7
	llama_model_loader: - kv 45: quantize.imatrix.file str = Qwen3.5-27B-GGUF/imatrix_unsloth.gguf
	llama_model_loader: - kv 46: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.5-27B.txt
	llama_model_loader: - kv 47: quantize.imatrix.entries_count u32 = 496
	llama_model_loader: - kv 48: quantize.imatrix.chunks_count u32 = 80
	llama_model_loader: - type f32: 353 tensors
	llama_model_loader: - type q8_0: 498 tensors
	print_info: file format = GGUF V3 (latest)
	print_info: file type = Q8_0
	print_info: file size = 26.62 GiB (8.50 BPW)
	load: 0 unused tokens
	load: printing all EOG tokens:
	load: - 248044 ('<\|endoftext\|>')
	load: - 248046 ('<\|im_end\|>')
	load: - 248063 ('<\|fim_pad\|>')
	load: - 248064 ('<\|repo_name\|>')
	load: - 248065 ('<\|file_sep\|>')
	load: special tokens cache size = 33
	load: token to piece cache size = 1.7581 MB
	print_info: arch = qwen35
	print_info: vocab_only = 0
	print_info: no_alloc = 0
	print_info: n_ctx_train = 262144
	print_info: n_embd = 5120
	print_info: n_embd_inp = 5120
	print_info: n_layer = 64
	print_info: n_head = 24
	print_info: n_head_kv = 4
	print_info: n_rot = 64
	print_info: n_swa = 0
	print_info: is_swa_any = 0
	print_info: n_embd_head_k = 256
	print_info: n_embd_head_v = 256
	print_info: n_gqa = 6
	print_info: n_embd_k_gqa = 1024
	print_info: n_embd_v_gqa = 1024
	print_info: f_norm_eps = 0.0e+00
	print_info: f_norm_rms_eps = 1.0e-06
	print_info: f_clamp_kqv = 0.0e+00
	print_info: f_max_alibi_bias = 0.0e+00
	print_info: f_logit_scale = 0.0e+00
	print_info: f_attn_scale = 0.0e+00
	print_info: n_ff = 17408
	print_info: n_expert = 0
	print_info: n_expert_used = 0
	print_info: n_expert_groups = 0
	print_info: n_group_used = 0
	print_info: causal attn = 1
	print_info: pooling type = 0
	print_info: rope type = 40
	print_info: rope scaling = linear
	print_info: freq_base_train = 10000000.0
	print_info: freq_scale_train = 1
	print_info: n_ctx_orig_yarn = 262144
	print_info: rope_yarn_log_mul = 0.0000
	print_info: rope_finetuned = unknown
	print_info: mrope sections = [11, 11, 10, 0]
	print_info: ssm_d_conv = 4
	print_info: ssm_d_inner = 6144
	print_info: ssm_d_state = 128
	print_info: ssm_dt_rank = 48
	print_info: ssm_n_group = 16
	print_info: ssm_dt_b_c_rms = 0
	print_info: model type = 27B
	print_info: model params = 26.90 B
	print_info: general.name = Qwen3.5-27B
	print_info: vocab type = BPE
	print_info: n_vocab = 248320
	print_info: n_merges = 247587
	print_info: BOS token = 11 ','
	print_info: EOS token = 248046 '<\|im_end\|>'
	print_info: EOT token = 248046 '<\|im_end\|>'
	print_info: PAD token = 248055 '<\|vision_pad\|>'
	print_info: LF token = 198 'Ċ'
	print_info: FIM PRE token = 248060 '<\|fim_prefix\|>'
	print_info: FIM SUF token = 248062 '<\|fim_suffix\|>'
	print_info: FIM MID token = 248061 '<\|fim_middle\|>'
	print_info: FIM PAD token = 248063 '<\|fim_pad\|>'
	print_info: FIM REP token = 248064 '<\|repo_name\|>'
	print_info: FIM SEP token = 248065 '<\|file_sep\|>'
	print_info: EOG token = 248044 '<\|endoftext\|>'
	print_info: EOG token = 248046 '<\|im_end\|>'
	print_info: EOG token = 248063 '<\|fim_pad\|>'
	print_info: EOG token = 248064 '<\|repo_name\|>'
	print_info: EOG token = 248065 '<\|file_sep\|>'
	print_info: max token length = 256