Instructions to use altomek/YiSM-34B-0rn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use altomek/YiSM-34B-0rn with Transformers:

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

pipe = pipeline("text-generation", model="altomek/YiSM-34B-0rn")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("altomek/YiSM-34B-0rn")
model = AutoModelForCausalLM.from_pretrained("altomek/YiSM-34B-0rn")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use altomek/YiSM-34B-0rn with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "altomek/YiSM-34B-0rn"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "altomek/YiSM-34B-0rn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/altomek/YiSM-34B-0rn

SGLang

How to use altomek/YiSM-34B-0rn 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 "altomek/YiSM-34B-0rn" \
    --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": "altomek/YiSM-34B-0rn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "altomek/YiSM-34B-0rn" \
        --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": "altomek/YiSM-34B-0rn",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use altomek/YiSM-34B-0rn with Docker Model Runner:
```
docker model run hf.co/altomek/YiSM-34B-0rn
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

intro music...

YiSM-34B-0rn

This is Yi Self Merged. I wanted model that will follow most instuctions yet preserve its base model nature.

Ingridients

Settings

I use max_seq_len 8K with alpha_value 2.65.

SillyTavern presets:

{
    "temp": 0.1,
    "temperature_last": true,
    "top_p": 1,
    "top_k": 0,
    "top_a": 0,
    "tfs": 1,
    "epsilon_cutoff": 0,
    "eta_cutoff": 0,
    "typical_p": 1,
    "min_p": 0,
    "rep_pen": 1.08,
    "rep_pen_range": 0,
    "no_repeat_ngram_size": 0,
    "penalty_alpha": 0,
    "num_beams": 1,
    "length_penalty": 1,
    "min_length": 0,
    "encoder_rep_pen": 1,
    "freq_pen": 0.01,
    "presence_pen": 0,
    "do_sample": true,
    "early_stopping": false,
    "add_bos_token": true,
    "truncation_length": 2048,
    "ban_eos_token": false,
    "skip_special_tokens": true,
    "streaming": true,
    "mirostat_mode": 0,
    "mirostat_tau": 5,
    "mirostat_eta": 0.1,
    "guidance_scale": 1,
    "negative_prompt": "",
    "grammar_string": "",
    "banned_tokens": "",
    "ignore_eos_token_aphrodite": false,
    "spaces_between_special_tokens_aphrodite": true,
    "sampler_order": [
        6,
        0,
        1,
        3,
        4,
        2,
        5
    ],
    "logit_bias": [],
    "n": 1,
    "rep_pen_size": 0,
    "genamt": 2048,
    "max_length": 8192
}

Terms and Conditions of Use

The following table outlines the primary characteristics and intended uses of my YiSM-34B-0rn models:

Model Type	Purpose	Target Users	Key Features
Censored	Suitable for general audiences and sensitive topics	Educational institutions, families, and individuals seeking age-appropriate content	Restricts explicit or mature material
Neutral (**this one)	Balances accessibility with openness	Universities, researchers, and curious minds	Encourages exploration and intellectual exchange
Uncensored	Ideal for adults and specialized fields	Professionals, experts, and advanced scholars	Offers unfiltered access to diverse viewpoints and knowledge

Please remember that all YiSM-34B-0rn models operate under the apache-2.0 license, so familiarize yourself with its terms and conditions before employing their content.

Quants

GGUF
8bpw
6.5bpw
4.65bpw
4bpw
3.2bpw -> Fits in 16GB VRAM but not recomended. Performance is significantly degraded in lower quants.
measurements --> ExLlamav2 measurments

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.65
AI2 Reasoning Challenge (25-Shot)	69.54
HellaSwag (10-Shot)	86.67
MMLU (5-Shot)	78.51
TruthfulQA (0-shot)	59.68
Winogrande (5-shot)	83.66
GSM8k (5-shot)	75.82

5th in 34B size range excluding "Private or deleted" or 8th with all models included as of 2024-06-10 ;P

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.15
IFEval (0-Shot)	42.84
BBH (3-Shot)	45.38
MATH Lvl 5 (4-Shot)	20.62
GPQA (0-shot)	16.22
MuSR (0-shot)	14.76
MMLU-PRO (5-shot)	41.06

Downloads last month: 16,349

Safetensors

Model size

34B params

Tensor type

F16

Model tree for altomek/YiSM-34B-0rn

01-ai/Yi-1.5-34B

01-ai/Yi-1.5-34B-Chat

Merge model

this model

Merges

1 model

Quantizations

9 models

Space using altomek/YiSM-34B-0rn 1

Collection including altomek/YiSM-34B-0rn

My models

Collection

6 items • Updated Mar 2

Evaluation results

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

69.540
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

86.670
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

78.510
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

59.680
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

83.660
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

75.820
strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

42.840
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

45.380