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FF_3/README.md
ModelHub XC 2e9dc165e0 初始化项目,由ModelHub XC社区提供模型
Model: francescofiamingo1/FF_3
Source: Original Platform
2026-05-31 05:07:17 +08:00

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---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- gpt2
- causal-lm
- ff-llm
---
# FF_3 — FF-LLM 2.02B
FF_3 is a 2.02B parameter language model trained from scratch.
## Model Details
- **Architecture**: GPT-2 decoder-only (custom)
- **Parameters**: 2,022,739,072
- **Vocabulary**: 50,257 (GPT-2 BPE tokenizer)
- **Context length**: 2,048 tokens
- **Training**: From scratch on 90B tokens
## Training Pipeline
1. **Pretraining**: 90B tokens (web + STEM data)
2. **SFT**: 760K examples + 100K high-quality examples
3. **DPO**: 38,863 preference pairs
4. **Distillation**: 20K examples from Qwen2.5-32B teacher
## Prompt Format
```
### System:
You are FF-LLM, a helpful assistant.
### Instruction:
{your question here}
### Response:
```
## Usage with Transformers
```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model = GPT2LMHeadModel.from_pretrained("ff-llm/FF_3")
tokenizer = GPT2Tokenizer.from_pretrained("ff-llm/FF_3")
prompt = (
"### System:\nYou are FF-LLM, a helpful assistant.\n\n"
"### Instruction:\nWhat is the capital of France?\n\n### Response:\n"
)
input_ids = tokenizer.encode(prompt, return_tensors="pt")
output = model.generate(
input_ids, max_new_tokens=256, do_sample=True,
temperature=0.7, top_p=0.9,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))
```
## Usage with Ollama
```bash
ollama run ff-llm/FF_3
```
## Limitations
- Weak mathematical reasoning
- May hallucinate on factual questions
- English only
## Training Cost
~\,000 total compute cost
Trained by a single researcher
## License
Apache 2.0