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RichardErkhov/TeamUNIVA_-_Komodo_6B_v2.0.0-gguf
TeamUNIVA_-_Komodo_6B_v2.0.0-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
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8% of all-time downloads
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.gguf82.3 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Komodo_6B_v2.0.0 - GGUF
| Name | Quant method | Size |
|---|---|---|
| Komodo_6B_v2.0.0.Q2_K.gguf | Q2_K | 2.24GB |
| Komodo_6B_v2.0.0.IQ3_XS.gguf | IQ3_XS | 2.48GB |
| Komodo_6B_v2.0.0.IQ3_S.gguf | IQ3_S | 2.6GB |
| Komodo_6B_v2.0.0.Q3_K_S.gguf | Q3_K_S | 2.59GB |
| Komodo_6B_v2.0.0.IQ3_M.gguf | IQ3_M | 2.69GB |
| Komodo_6B_v2.0.0.Q3_K.gguf | Q3_K | 2.86GB |
| Komodo_6B_v2.0.0.Q3_K_M.gguf | Q3_K_M | 2.86GB |
| Komodo_6B_v2.0.0.Q3_K_L.gguf | Q3_K_L | 3.08GB |
| Komodo_6B_v2.0.0.IQ4_XS.gguf | IQ4_XS | 3.18GB |
| Komodo_6B_v2.0.0.Q4_0.gguf | Q4_0 | 3.32GB |
| Komodo_6B_v2.0.0.IQ4_NL.gguf | IQ4_NL | 3.35GB |
| Komodo_6B_v2.0.0.Q4_K_S.gguf | Q4_K_S | 3.34GB |
| Komodo_6B_v2.0.0.Q4_K.gguf | Q4_K | 3.5GB |
| Komodo_6B_v2.0.0.Q4_K_M.gguf | Q4_K_M | 3.5GB |
| Komodo_6B_v2.0.0.Q4_1.gguf | Q4_1 | 3.66GB |
| Komodo_6B_v2.0.0.Q5_0.gguf | Q5_0 | 4.0GB |
| Komodo_6B_v2.0.0.Q5_K_S.gguf | Q5_K_S | 4.0GB |
| Komodo_6B_v2.0.0.Q5_K.gguf | Q5_K | 4.09GB |
| Komodo_6B_v2.0.0.Q5_K_M.gguf | Q5_K_M | 4.09GB |
| Komodo_6B_v2.0.0.Q5_1.gguf | Q5_1 | 4.34GB |
| Komodo_6B_v2.0.0.Q6_K.gguf | Q6_K | 4.72GB |
| Komodo_6B_v2.0.0.Q8_0.gguf | Q8_0 | 6.12GB |
license: apache-2.0 language:
beomi/Yi-Ko-6B
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "TeamUNIVA/Komodo_6B_v2.0.0"
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
text = '''<|system|>
당신은 사용자의 질문에 친절하게 답변을 하는 챗봇입니다.
<|user|>
안녕하세요?
<|bot|>
'''
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Use the code below to get started with the model.
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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