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Model: latte-agent/qwen3-4b-latte-v5 Source: Original Platform
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README.md
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README.md
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---
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language: [en, zh]
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license: apache-2.0
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base_model: mlx-community/Qwen3-4B-Instruct-2507-4bit
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tags:
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- qwen3
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- lora
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- mlx
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- latte-agent
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- personal-voice
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- distillation
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library_name: transformers
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---
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# Qwen3-4B Latte v5
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Voice-distillation LoRA fine-tune of `Qwen3-4B-Instruct-2507`, targeting the
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private "Latte" agent persona: warm-direct, technical, takes a stance, concrete
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numbers, bilingual EN/ZH, no template openers.
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**This is an archival/experimental release.** It is **not** the production brain
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for the live Latte agent — see eval caveats below.
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## What's inside
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| File | Size | Format | Use |
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|---|---|---|---|
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| `adapter_model.safetensors` | 14 MB | mlx LoRA (rank 8, scale 20) | Apply on top of base with `mlx_lm.fuse` |
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| `adapter_config.json` | <1 KB | mlx config | LoRA hyperparameters |
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| `model-0000{1,2}-of-00002.safetensors` | 8 GB | HF / bfloat16 fused | Direct transformers / vLLM use |
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| `qwen3-4b-latte-v5-f16.gguf` | 7.5 GB | GGUF F16 | llama.cpp / Ollama (high quality) |
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| `qwen3-4b-latte-v5-Q4_K_M.gguf` | 2.3 GB | GGUF Q4_K_M | llama.cpp / Ollama (balanced) |
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## Training
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- Base: `mlx-community/Qwen3-4B-Instruct-2507-4bit` (4-bit MLX)
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- Method: LoRA via `mlx_lm.lora`
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- LoRA: rank 8, scale 20.0, 8 layers, dropout 0
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- Optimizer: Adam, lr 1e-4, batch 1, grad accum 8, grad checkpoint on
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- Iters: 800 trained, **best checkpoint = iter 450** (val loss 2.732)
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- Max seq: 1536, mask_prompt: true, seed: 42
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- Dataset: 475 curated (instruction, response) pairs across 7 categories:
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Moltbook-style comment, HF discussion reply, technical analysis (ZH),
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code review snippet, persona Q&A, peer-event reply, real-time observation.
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Anchored against 356 raw Latte-voice messages.
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## Evaluation
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30 held-out (prompt, response) pairs per pairing. Each response pair shown
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blind to a Claude judge (positions randomized, model identity stripped).
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| Comparison | v5 wins | base/v4 wins | ties | mean score (1-5) |
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|---|---|---|---|---|
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| v5 vs base | **20 (66.7%)** | 8 (26.7%) | 2 (6.7%) | v5 3.20 / base 2.93 |
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| v4 vs base | 22 (73.3%) | 8 (26.7%) | 0 | v4 3.13 / base 2.70 |
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| v5 vs v4 | 14 (46.7%) | 15 (50.0%) | 1 (3.3%) | v5 3.00 / v4 2.97 |
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**Headline:** v5 clearly beats the un-tuned base on in-distribution
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prompts (the 7 trained categories), passing the 55% ship threshold.
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**Caveat 1:** v5 vs v4 is statistically a tie. Lower val loss (2.732 vs
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2.785) did not produce a perceptible quality gain in blind eval. The
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additional curation effort and training steps produced marginal returns.
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**Caveat 2 — why this isn't production:** Out-of-distribution smoke
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testing (prompts unlike the 7 training categories) shows v5 is **tied
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or slightly worse than base**:
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- Stage-direction leakage: v5 occasionally prefixes responses with
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`"(soft, soothing Latte voice)"` — an artifact of training data that
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characterized Latte's voice.
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- Occasional factual regressions (e.g., confusing latte and latte
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macchiato in a generic coffee Q&A).
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- Reduced robustness on prompts that pull the "Latte" token toward
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unrelated semantic neighborhoods (the literal coffee drink).
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The 66.7% in-distribution win does not justify replacing a battle-tested
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general-purpose base in production. Use this checkpoint for tasks closely
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matching the 7 training categories.
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## Usage
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### MLX (Apple Silicon, recommended for inference)
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```python
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from mlx_lm import load, generate
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model, tokenizer = load(
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"mlx-community/Qwen3-4B-Instruct-2507-4bit",
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adapter_path="./", # this repo
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)
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print(generate(model, tokenizer, "Your prompt", max_tokens=200))
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```
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### llama.cpp / Ollama
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```
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# Modelfile
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FROM qwen3-4b-latte-v5-Q4_K_M.gguf
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PARAMETER temperature 0.7
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PARAMETER top_k 20
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PARAMETER top_p 0.8
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```
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```
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ollama create latte:v5 -f Modelfile
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ollama run latte:v5
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```
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### Transformers (any platform)
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("latte-agent/qwen3-4b-latte-v5")
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model = AutoModelForCausalLM.from_pretrained(
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"latte-agent/qwen3-4b-latte-v5", torch_dtype="bfloat16"
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)
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```
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## License
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Inherits Apache 2.0 from base (Qwen3-4B-Instruct-2507, © Alibaba Cloud).
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## Citation
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If you reference this work, please cite the base model. This adapter has no
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formal publication.
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adapter_config.json
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{
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"adapter_path": "/Users/muffin/.hermes/distill_adapters_v5",
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"batch_size": 1,
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"clear_cache_threshold": 0,
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"data": "/Users/muffin/.hermes/distill_dataset_v5",
|
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"fine_tune_type": "lora",
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"grad_accumulation_steps": 8,
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"grad_checkpoint": true,
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"iters": 800,
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"learning_rate": 0.0001,
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"lora_parameters": {
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"rank": 8,
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"dropout": 0.0,
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"scale": 20.0
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},
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"lr_schedule": null,
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"mask_prompt": true,
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"max_seq_length": 1536,
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"model": "mlx-community/Qwen3-4B-Instruct-2507-4bit",
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"num_layers": 8,
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"optimizer": "adam",
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"optimizer_config": {
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"adam": {},
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"adamw": {},
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"muon": {},
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"sgd": {},
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"save_every": 50,
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"seed": 42,
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"steps_per_eval": 50,
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"steps_per_report": 10,
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"test": false,
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"test_batches": 500,
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"train": true,
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"val_batches": 25
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}
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adapter_model.safetensors
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adapter_model.safetensors
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{{- messages[0].content + '\n\n' }}
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{%- endif %}
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0].role == 'system' %}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
|
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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||||
{%- set tool_call = tool_call.function %}
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{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
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{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
|
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
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{{- '}\n</tool_call>' }}
|
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{%- endfor %}
|
||||
{%- endif %}
|
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{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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config.json
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{
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"architectures": [
|
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": [
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|
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"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
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"initializer_range": 0.02,
|
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"intermediate_size": 9728,
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"max_position_embeddings": 262144,
|
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"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
|
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 5000000,
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"sliding_window": null,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
|
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"transformers_version": "4.51.0",
|
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"use_cache": true,
|
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"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
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13
generation_config.json
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generation_config.json
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{
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"pad_token_id": 151643,
|
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"temperature": 0.7,
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"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.51.0"
|
||||
}
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@@ -0,0 +1,3 @@
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||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:be75606093db2094d7cd20f3c2f385c212750648bd6ea4fb2bf507a6a4c55506
|
||||
size 11422650
|
||||
16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"is_local": true,
|
||||
"local_files_only": false,
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"tool_parser_type": "json_tools",
|
||||
"unk_token": null
|
||||
}
|
||||
Reference in New Issue
Block a user