Instructions to use TaylorAI/Flash-Llama-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TaylorAI/Flash-Llama-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TaylorAI/Flash-Llama-7B", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TaylorAI/Flash-Llama-7B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TaylorAI/Flash-Llama-7B", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TaylorAI/Flash-Llama-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TaylorAI/Flash-Llama-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaylorAI/Flash-Llama-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TaylorAI/Flash-Llama-7B

SGLang

How to use TaylorAI/Flash-Llama-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TaylorAI/Flash-Llama-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaylorAI/Flash-Llama-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TaylorAI/Flash-Llama-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TaylorAI/Flash-Llama-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TaylorAI/Flash-Llama-7B with Docker Model Runner:
```
docker model run hf.co/TaylorAI/Flash-Llama-7B
```

andersonbcdefg commited on Aug 28, 2023

Commit

c5cda5b

1 Parent(s): 422979b

Upload modeling_flash_llama.py

Browse files

Files changed (1) hide show

modeling_flash_llama.py +1 -1

modeling_flash_llama.py CHANGED Viewed

@@ -363,7 +363,7 @@ class LlamaAttention(nn.Module):
         past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None
         # no padding tokens, more efficient
-        attn_dtype = self.o_proj.weight.dtype
         attn_outputs = flash_attn_kvpacked_func(
             q.type(attn_dtype), kv.type(attn_dtype), dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)

         past_key_value = (past_kv, past_len+q.size(1)) if use_cache else None
         # no padding tokens, more efficient
+        attn_dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
         attn_outputs = flash_attn_kvpacked_func(
             q.type(attn_dtype), kv.type(attn_dtype), dropout_p=0.0, softmax_scale=1.0/self.norm_factor, causal=(not has_layer_past), return_attn_probs=output_attentions)