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RichardErkhov/
belyakoff_-_SmolLM2-360M-Instruct-FT-gguf

Name: belyakoff_-_SmolLM2-360M-Instruct-FT-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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SmolLM2-360M-Instruct-FT - GGUF

Model creator: https://huggingface.co/belyakoff/
Original model: https://huggingface.co/belyakoff/SmolLM2-360M-Instruct-FT/

Name	Quant method	Size
SmolLM2-360M-Instruct-FT.Q2_K.gguf	Q2_K	0.2GB
SmolLM2-360M-Instruct-FT.Q3_K_S.gguf	Q3_K_S	0.2GB
SmolLM2-360M-Instruct-FT.Q3_K.gguf	Q3_K	0.22GB
SmolLM2-360M-Instruct-FT.Q3_K_M.gguf	Q3_K_M	0.22GB
SmolLM2-360M-Instruct-FT.Q3_K_L.gguf	Q3_K_L	0.23GB
SmolLM2-360M-Instruct-FT.IQ4_XS.gguf	IQ4_XS	0.21GB
SmolLM2-360M-Instruct-FT.Q4_0.gguf	Q4_0	0.21GB
SmolLM2-360M-Instruct-FT.IQ4_NL.gguf	IQ4_NL	0.21GB
SmolLM2-360M-Instruct-FT.Q4_K_S.gguf	Q4_K_S	0.24GB
SmolLM2-360M-Instruct-FT.Q4_K.gguf	Q4_K	0.25GB
SmolLM2-360M-Instruct-FT.Q4_K_M.gguf	Q4_K_M	0.25GB
SmolLM2-360M-Instruct-FT.Q4_1.gguf	Q4_1	0.23GB
SmolLM2-360M-Instruct-FT.Q5_0.gguf	Q5_0	0.25GB
SmolLM2-360M-Instruct-FT.Q5_K_S.gguf	Q5_K_S	0.26GB
SmolLM2-360M-Instruct-FT.Q5_K.gguf	Q5_K	0.27GB
SmolLM2-360M-Instruct-FT.Q5_K_M.gguf	Q5_K_M	0.27GB
SmolLM2-360M-Instruct-FT.Q5_1.gguf	Q5_1	0.27GB
SmolLM2-360M-Instruct-FT.Q6_K.gguf	Q6_K	0.34GB
SmolLM2-360M-Instruct-FT.Q8_0.gguf	Q8_0	0.36GB

Original model description:

library_name: transformers license: apache-2.0 datasets:

Vikhrmodels/GrandMaster-PRO-MAX language:
ru base_model:
HuggingFaceTB/SmolLM2-360M-Instruct pipeline_tag: text-generation tags:
rag

Versions

v1: SFT -- 7658aab7702e56d9f5fa3b33bf7adcdae92f536b

Example

python

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "belyakoff/SmolLM2-360M-Instruct-FT"

device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)

messages = [{"role": "user", "content": "Столица России?"}]
input_text=tokenizer.apply_chat_template(messages, tokenize=False)
print(input_text)
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0]))
# Столица России — Москва. Это один из самых известных и культурно значимых городов в мире.

Limitations

Don't change system prompt. Changing system prompt will make the model go crazy.

RichardErkhov/belyakoff_-_SmolLM2-360M-Instruct-FT-gguf

Original model description:

Versions

Example

Limitations

RichardErkhov/
belyakoff_-_SmolLM2-360M-Instruct-FT-gguf