feeltheAGI/maverick-sharegpt
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How to use feeltheAGI/maverick-llama3-8B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="feeltheAGI/maverick-llama3-8B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("feeltheAGI/maverick-llama3-8B")
model = AutoModelForCausalLM.from_pretrained("feeltheAGI/maverick-llama3-8B")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))How to use feeltheAGI/maverick-llama3-8B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "feeltheAGI/maverick-llama3-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "feeltheAGI/maverick-llama3-8B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/feeltheAGI/maverick-llama3-8B
How to use feeltheAGI/maverick-llama3-8B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "feeltheAGI/maverick-llama3-8B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "feeltheAGI/maverick-llama3-8B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'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 "feeltheAGI/maverick-llama3-8B" \
--host 0.0.0.0 \
--port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "feeltheAGI/maverick-llama3-8B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use feeltheAGI/maverick-llama3-8B with Docker Model Runner:
docker model run hf.co/feeltheAGI/maverick-llama3-8B
model is using ChatML prompt template format.
example:
<|im_start|>system
You are Maverick, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
🏆 Evaluation
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| eq_bench | 2.1 | none | 0 | eqbench | 56.8710 | ± | 2.8946 |
| none | 0 | percent_parseable | 99.4152 | ± | 0.5848 |
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| winogrande | 1 | none | 0 | acc | 0.7372 | ± | 0.0124 |
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| gsm8k | 3 | strict-match | 5 | exact_match | 0.5694 | ± | 0.0136 |
| flexible-extract | 5 | exact_match | 0.5701 | ± | 0.0136 |
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| mathqa | 1 | none | 0 | acc | 0.3930 | ± | 0.0089 |
| none | 0 | acc_norm | 0.3869 | ± | 0.0089 |
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT