Best models made
Collection
this is a collection of the models I had tested a bit,and found interesting • 3 items • Updated • 1
How to use Pedro13543/good_mix_r1 with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="Pedro13543/good_mix_r1")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Pedro13543/good_mix_r1")
model = AutoModelForCausalLM.from_pretrained("Pedro13543/good_mix_r1")
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 Pedro13543/good_mix_r1 with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pedro13543/good_mix_r1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Pedro13543/good_mix_r1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/Pedro13543/good_mix_r1
How to use Pedro13543/good_mix_r1 with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "Pedro13543/good_mix_r1" \
--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": "Pedro13543/good_mix_r1",
"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 "Pedro13543/good_mix_r1" \
--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": "Pedro13543/good_mix_r1",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use Pedro13543/good_mix_r1 with Docker Model Runner:
docker model run hf.co/Pedro13543/good_mix_r1
This is a merge of pre-trained language models created using mergekit. This model is smart but a bit limited in creativity,but hey,it can think in first person,like r1,and also write quite good stories.
This model was merged using the Model Stock merge method using vicgalle/Humanish-Roleplay-Llama-3.1-8B as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.3
- model: Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2
- model: Undi95/Llama3-Unholy-8B-OAS
- model : Undi95/Meta-Llama-3.1-8B-Claude
- model : vicgalle/Humanish-Roleplay-Llama-3.1-8B
- model: vicgalle/Roleplay-Hermes-3-Llama-3.1-8B
- model: TheDrummer/Llama-3SOME-8B-v2
- model: Skywork/Skywork-o1-Open-Llama-3.1-8B
- model: Solshine/reflection-llama-3.1-8B-Solshine-Full
- model: deepseek-ai/DeepSeek-R1-Distill-Llama-8B
merge_method: model_stock
base_model: vicgalle/Humanish-Roleplay-Llama-3.1-8B
parameters:
normalize: false
int8_mask: true
dtype: float16