|
|
--- |
|
|
license: llama3.3 |
|
|
library_name: transformers |
|
|
pipeline_tag: text-generation |
|
|
base_model: meta-llama/Llama-3.3-70B-Instruct |
|
|
tags: |
|
|
- llama |
|
|
- llama-3 |
|
|
- code |
|
|
- instruct |
|
|
- fine-tuned |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
|
|
|
# Phind-70B |
|
|
|
|
|
Phind-70B is a fine-tuned version of [Llama 3.3 70B Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), optimized for code generation, technical reasoning, and general instruction following. |
|
|
|
|
|
## Model Details |
|
|
|
|
|
| Attribute | Details | |
|
|
|-----------|---------| |
|
|
| **Base Model** | meta-llama/Llama-3.3-70B-Instruct | |
|
|
| **Model Type** | Causal Language Model | |
|
|
| **Parameters** | 70 Billion | |
|
|
| **Context Length** | 128K tokens | |
|
|
| **Language** | English | |
|
|
| **License** | Llama 3.3 Community License | |
|
|
|
|
|
## Intended Use |
|
|
|
|
|
Phind-70B is designed for: |
|
|
|
|
|
- **Code generation** across multiple programming languages |
|
|
- **Technical problem-solving** and debugging |
|
|
- **General instruction following** and reasoning tasks |
|
|
- **Multi-turn conversations** requiring context retention |
|
|
|
|
|
## How to Use |
|
|
|
|
|
### With Transformers |
|
|
|
|
|
```python |
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
import torch |
|
|
|
|
|
model_id = "Phind/Phind-70B" |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained(model_id) |
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
|
model_id, |
|
|
torch_dtype=torch.bfloat16, |
|
|
device_map="auto", |
|
|
) |
|
|
|
|
|
messages = [ |
|
|
{"role": "system", "content": "You are Phind, an intelligent assistant that helps with programming and technical questions."}, |
|
|
{"role": "user", "content": "Write a Python function to find the longest palindromic substring."}, |
|
|
] |
|
|
|
|
|
input_ids = tokenizer.apply_chat_template( |
|
|
messages, |
|
|
add_generation_prompt=True, |
|
|
return_tensors="pt" |
|
|
).to(model.device) |
|
|
|
|
|
outputs = model.generate( |
|
|
input_ids, |
|
|
max_new_tokens=1024, |
|
|
do_sample=True, |
|
|
temperature=0.7, |
|
|
top_p=0.9, |
|
|
) |
|
|
|
|
|
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True) |
|
|
print(response) |
|
|
``` |
|
|
|
|
|
## Chat Template |
|
|
|
|
|
This model uses the Llama 3 chat format: |
|
|
|
|
|
``` |
|
|
<|begin_of_text|><|start_header_id|>system<|end_header_id|> |
|
|
|
|
|
{system_message}<|eot_id|><|start_header_id|>user<|end_header_id|> |
|
|
|
|
|
{user_message}<|eot_id|><|start_header_id|>assistant<|end_header_id|} |
|
|
|
|
|
{assistant_response}<|eot_id|> |
|
|
``` |
|
|
|
|
|
## Hardware Requirements |
|
|
|
|
|
| Precision | VRAM Required | |
|
|
|-----------|---------------| |
|
|
| FP16/BF16 | ~140 GB | |
|
|
| INT8 | ~70 GB | |
|
|
| INT4 | ~35 GB | |
|
|
|
|
|
For inference, we recommend using multiple GPUs with tensor parallelism or quantized versions for consumer hardware. |
|
|
|
|
|
## Limitations |
|
|
|
|
|
- May occasionally generate incorrect or misleading information |
|
|
- Not suitable for production use without additional safety measures |
|
|
- Performance may vary on tasks outside the training distribution |
|
|
- Should not be used for generating harmful, illegal, or unethical content |
|
|
|
|
|
## Acknowledgments |
|
|
|
|
|
This model builds upon the excellent work by Meta on the Llama 3.3 model family. We are grateful for their contributions to open-source AI. |
