Mistral-7B
Collection
5 items β’ Updated β’ 1
How to use FallenMerick/Smart-Lemon-Cookie-7B with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="FallenMerick/Smart-Lemon-Cookie-7B")
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe(messages) # Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("FallenMerick/Smart-Lemon-Cookie-7B")
model = AutoModelForCausalLM.from_pretrained("FallenMerick/Smart-Lemon-Cookie-7B")
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 FallenMerick/Smart-Lemon-Cookie-7B with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FallenMerick/Smart-Lemon-Cookie-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "FallenMerick/Smart-Lemon-Cookie-7B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'docker model run hf.co/FallenMerick/Smart-Lemon-Cookie-7B
How to use FallenMerick/Smart-Lemon-Cookie-7B with SGLang:
# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
--model-path "FallenMerick/Smart-Lemon-Cookie-7B" \
--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": "FallenMerick/Smart-Lemon-Cookie-7B",
"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 "FallenMerick/Smart-Lemon-Cookie-7B" \
--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": "FallenMerick/Smart-Lemon-Cookie-7B",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'How to use FallenMerick/Smart-Lemon-Cookie-7B with Docker Model Runner:
docker model run hf.co/FallenMerick/Smart-Lemon-Cookie-7B
image courtesy of @matchaaaaa
This is a merge of pre-trained language models created using mergekit.
GGUF quants:
This model was merged using the TIES merge method using MTSAIR/multi_verse_model as a base.
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: SanjiWatsuki/Silicon-Maid-7B
parameters:
density: 1.0
weight: 1.0
- model: SanjiWatsuki/Kunoichi-7B
parameters:
density: 0.4
weight: 1.0
- model: KatyTheCutie/LemonadeRP-4.5.3
parameters:
density: 0.6
weight: 1.0
merge_method: ties
base_model: MTSAIR/multi_verse_model
parameters:
normalize: true
dtype: float16
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.16 |
| AI2 Reasoning Challenge (25-Shot) | 66.30 |
| HellaSwag (10-Shot) | 85.53 |
| MMLU (5-Shot) | 64.69 |
| TruthfulQA (0-shot) | 60.66 |
| Winogrande (5-shot) | 77.74 |
| GSM8k (5-shot) | 54.06 |