---
license: apache-2.0
language:
- en
- zh
tags:
- qwen3
- qwen
- causal-lm
- triton
- fine-tune
- safetensors
- bfloat16
pipeline_tag: text-generation
base_model: Qwen/Qwen3-8B
---
> Note:This project represents independent research conducted on personal compute resources (rented from Modal.com) and is not associated with my employer or organization
# Qwen3-8B-Triton-Finetune

A fine-tuned version of **Qwen3-8B** (the latest generation of Qwen large language models by Alibaba Cloud), further trained using a **Triton-based** fine-tuning pipeline. This model retains the strong reasoning and instruction-following capabilities of Qwen3-8B while adding task-specific adaptations via custom Triton kernels.

## Model Details

- **Base Model**: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
- **Architecture**: Qwen3ForCausalLM
- **Parameters**: 8.19B (BF16)
- **Hidden Size**: 4096
- **Intermediate Size**: 12288
- **Attention Heads**: 32 (8 KV heads, grouped-query attention)
- **Layers**: 36 (full attention, no sliding window)
- **Max Position Embeddings**: 40,960 tokens
- **Vocabulary Size**: 151,936
- **Attention Mechanism**: RoPE (Rotary Position Embeddings, theta=1,000,000)
- **Activation**: SiLU (SwigLU)
- **Precision**: bfloat16
- **Chat Template**: im_start/im_end format with tool calling support

## Capabilities

- **Text generation & completion**: General-purpose language understanding and generation
- **Instruction following**: Fine-tuned for chat and instructions
- **Multi-step tool calling**: Built-in tool/function calling support via the chat template
- **Extended context**: Supports up to ~40K tokens of context
- **Reasoning**: Supports think/reasoning blocks (`<think>...</think>`) in generation

## Files

| File | Description |
|------|-------------|
| `config.json` | Model architecture configuration |
| `generation_config.json` | Generation parameters |
| `model-*.safetensors` | Model weights (sharded across 4 files) |
| `model.safetensors.index.json` | Weight shard index |
| `tokenizer.json` | Tokenizer |
| `tokenizer_config.json` | Tokenizer configuration |
| `vocab.json` | Vocabulary |
| `merges.txt` | BPE merges |
| `added_tokens.json` | Special/added tokens |
| `special_tokens_map.json` | Special token mapping |
| `chat_template.jinja` | Chat template (Jinja) |

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "edwixx/qwen3-8b-triton-finetune",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("edwixx/qwen3-8b-triton-finetune")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain what fine-tuning with Triton means."}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## License

Apache 2.0 (inherited from Qwen3-8B).

## Citation

```bibtex
@misc{edwixx-qwen3-8b-triton-finetune,
  author = {Anurag Kanade},
  title = {qwen3-8b-triton-finetune},
  year = {2026},
  publisher = {Hugging Face},
  journal = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/edwixx/qwen3-8b-triton-finetune}}
}
```