Instructions to use mayacinka/yam-sam-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mayacinka/yam-sam-7B with Transformers:

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

pipe = pipeline("text-generation", model="mayacinka/yam-sam-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mayacinka/yam-sam-7B")
model = AutoModelForCausalLM.from_pretrained("mayacinka/yam-sam-7B")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mayacinka/yam-sam-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mayacinka/yam-sam-7B

SGLang

How to use mayacinka/yam-sam-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 "mayacinka/yam-sam-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": "mayacinka/yam-sam-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 "mayacinka/yam-sam-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": "mayacinka/yam-sam-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mayacinka/yam-sam-7B with Docker Model Runner:
```
docker model run hf.co/mayacinka/yam-sam-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

yam-sam-7B

yam-sam-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: yam-peleg/Experiment27-7B
    # No parameters necessary for base model
  - model: cognitivecomputations/samantha-mistral-7b
    parameters:
      weight: 0.3
      density: 0.8
  - model: CorticalStack/shadow-clown-7B-dare
    parameters:
      weight: 0.1
      density: 0.8
  - model: yam-peleg/Experiment26-7B
    parameters:
      weight: 0.6
      density: 0.8
merge_method: dare_ties
base_model: yam-peleg/Experiment27-7B
parameters:
  int8_mask: true
dtype: bfloat16
random_seed: 0

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mayacinka/yam-sam-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	74.58
AI2 Reasoning Challenge (25-Shot)	70.90
HellaSwag (10-Shot)	87.92
MMLU (5-Shot)	65.39
TruthfulQA (0-shot)	71.30
Winogrande (5-shot)	83.03
GSM8k (5-shot)	68.92

Downloads last month: 65

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for mayacinka/yam-sam-7B

CorticalStack/shadow-clown-7B-dare

QuixiAI/samantha-mistral-7b

yam-peleg/Experiment26-7B

Merge model

this model

Quantizations

2 models

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

70.900
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

87.920
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

65.390
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

71.300
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.030
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

68.920