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Model: sandeeprdy1729/TIMPS-Coder-0.5B Source: Original Platform
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README.md
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---
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language:
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- en
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
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tags:
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- code
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- bug-fixing
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- code-review
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- qwen2
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- lora
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- mlx
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- ollama
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- chatml
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pipeline_tag: text-generation
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library_name: transformers
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---
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# TIMPS-Coder v3 — Elite Bug-Fixing Assistant (0.5B)
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> A 0.5B parameter coding model fine-tuned to **think before it codes** — specialising in bug
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> analysis, code review, algorithm problem-solving, and agentic planning.
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> Built by [Sandeep Reddy](https://github.com/Sandeeprdy1729) · TIMPS · Made in India 🇮🇳
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[](https://huggingface.co/sandeeprdy1729/TIMPS-Coder-0.5B)
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[](https://ollama.com/sandeeprdy1729/timps-coder)
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[](LICENSE)
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[-brightgreen)](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/benchmark_results.json)
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## Model Summary
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| Field | Value |
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|---|---|
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| **Base model** | `Qwen/Qwen2.5-Coder-0.5B-Instruct` (Alibaba Cloud) |
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| **Architecture** | Qwen2 Transformer — 494M parameters |
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| **Fine-tuning method** | LoRA (rank=16, 16 layers) via MLX-LM |
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| **Context window** | 4096 tokens |
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| **Quantization** | Q4_K_M GGUF (Ollama) / BF16 safetensors (HuggingFace) |
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| **Chat template** | ChatML (`<|im_start|>` / `<|im_end|>`) |
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| **License** | Apache 2.0 |
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| **Training hardware** | Apple M-series (Mac M1/M2/M3, 8 GB RAM) |
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## Benchmark Results — 25 Tests, 5 Dimensions
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Evaluated on [3_benchmark_ollama.py](https://github.com/Sandeeprdy1729/TIMPS-Coder/blob/main/3_benchmark_ollama.py).
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Scoring: **2 pts** = complete correct answer with code · **1 pt** = partial · **0** = wrong/refused.
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| Dimension | Score | % |
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|---|---|---|
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| 🐛 Bug Fix | 9 / 10 | **90%** |
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| 🔧 SWE / Repo-level | 9 / 10 | **90%** |
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| ⚡ Algorithms | 9 / 10 | **90%** |
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| 🔍 Code Review | 8 / 10 | **80%** |
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| 🤖 Agentic Reasoning | 9 / 10 | **90%** |
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| **TOTAL** | **44 / 50** | **88%** |
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## Quick Start
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### Ollama (recommended)
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```bash
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ollama pull sandeeprdy1729/timps-coder
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ollama run sandeeprdy1729/timps-coder
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```
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### Python (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
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tokenizer = AutoTokenizer.from_pretrained("sandeeprdy1729/TIMPS-Coder-0.5B")
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messages = [
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{"role": "system", "content": "You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases."},
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{"role": "user", "content": "Fix: `data['user']['email']` throws KeyError when email is absent."},
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt")
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out = model.generate(**inputs, max_new_tokens=700, temperature=0.1, do_sample=True)
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print(tokenizer.decode(out[0], skip_special_tokens=True))
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```
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### MLX (Mac Apple Silicon)
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```bash
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pip install mlx-lm
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mlx_lm.generate \
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--model sandeeprdy1729/TIMPS-Coder-0.5B \
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--max-tokens 700 --temp 0.1 \
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--prompt '<|im_start|>system
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You are TIMPS-Coder v3. THINK through the root cause, FIX with complete code, VERIFY edge cases.<|im_end|>
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<|im_start|>user
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Fix the race condition: two threads increment self.count += 1 simultaneously.<|im_end|>
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<|im_start|>assistant
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'
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```
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## Training Details
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### Fine-tuning Configuration
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| Parameter | Value |
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|---|---|
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| Base model | `Qwen/Qwen2.5-Coder-0.5B-Instruct` |
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| Fine-tuning method | LoRA (Supervised Fine-Tuning) |
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| LoRA rank | 16 |
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| Learning rate | 5e-6 |
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| Iterations | 3,000 |
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| Batch size | 1 (grad accum ×4) |
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| Max sequence length | 2048 tokens |
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| Framework | MLX-LM on Apple Silicon |
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| Peak RAM | ~5.5 GB |
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### Training Data
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| Dataset | Type | Approx. Samples |
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|---|---|---|
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| `newfacade/LeetCodeDataset` | Algorithm problems with solutions | ~2,500 |
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| `SWE-bench/SWE-bench_Verified` | Real GitHub issue → patch | ~400 |
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| `TIGER-Lab/SWE-Next-SFT-Trajectories` | Agentic edit traces | ~2,000 |
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| `WaltonFuture/agentic-sft-new` | Tool use + bash planning | ~3,000 |
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| Custom TIMPS bug-fix corpus | Hand-curated bug/fix pairs | ~500 |
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| **Total** | | **~8,400 samples** |
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All samples formatted in ChatML with `THINK → FIX → VERIFY` answer structure.
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## Capabilities
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| Does well | Limitations |
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|---|---|
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| Bug root-cause analysis with explanation | Complex multi-file refactors |
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| SQL injection, race condition, memory leak detection | May miss subtle business-logic bugs |
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| O-notation analysis and algorithm optimisation | Not a replacement for static analysis tools |
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| LeetCode medium-level algorithm problems | Hard competitive programming problems |
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| GitHub Actions / CI YAML generation | Not trained on Terraform, CDK |
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## Usage Tips
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- **Temperature**: Keep at `0.1` — higher values increase hallucination on a 0.5B model
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- **Context**: Include the full function/class when asking for a bug fix
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- **Verification**: Always test generated code. Even at 88% accuracy, edge cases exist
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- **System prompt**: Required for best results — see the Quick Start examples above
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## Training Code
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Full training pipeline available at:
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[https://github.com/Sandeeprdy1729/TIMPS-Coder](https://github.com/Sandeeprdy1729/TIMPS-Coder)
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## License
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Apache 2.0 — free to use, modify, and distribute commercially.
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Base model (Qwen2.5-Coder-0.5B-Instruct) is also Apache 2.0.
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54
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'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# 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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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
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{%- endif %}
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58
config.json
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}
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generation_config.json
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|
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"model.layers.9.mlp.down_proj.weight": "model.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model.safetensors",
|
||||
"model.norm.weight": "model.safetensors"
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
|
||||
size 11422356
|
||||
203
tokenizer_config.json
Normal file
203
tokenizer_config.json
Normal file
@@ -0,0 +1,203 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [],
|
||||
"is_local": false,
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|PAD_TOKEN|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<|PAD_TOKEN|>",
|
||||
"single_word": false,
|
||||
"lstrip": false,
|
||||
"rstrip": false,
|
||||
"normalized": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||
}
|
||||
Reference in New Issue
Block a user