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RichardErkhov/1412312anonymous_-_AssertSolver-gguf
1412312anonymous_-_AssertSolver-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
3
1% of all-time downloads
All-time downloads
478
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.gguf88.8 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
AssertSolver - GGUF
| Name | Quant method | Size |
|---|---|---|
| AssertSolver.Q2_K.gguf | Q2_K | 2.36GB |
| AssertSolver.IQ3_XS.gguf | IQ3_XS | 2.61GB |
| AssertSolver.IQ3_S.gguf | IQ3_S | 2.75GB |
| AssertSolver.Q3_K_S.gguf | Q3_K_S | 2.75GB |
| AssertSolver.IQ3_M.gguf | IQ3_M | 2.9GB |
| AssertSolver.Q3_K.gguf | Q3_K | 3.07GB |
| AssertSolver.Q3_K_M.gguf | Q3_K_M | 3.07GB |
| AssertSolver.Q3_K_L.gguf | Q3_K_L | 3.35GB |
| AssertSolver.IQ4_XS.gguf | IQ4_XS | 3.4GB |
| AssertSolver.Q4_0.gguf | Q4_0 | 3.56GB |
| AssertSolver.IQ4_NL.gguf | IQ4_NL | 3.59GB |
| AssertSolver.Q4_K_S.gguf | Q4_K_S | 3.59GB |
| AssertSolver.Q4_K.gguf | Q4_K | 3.8GB |
| AssertSolver.Q4_K_M.gguf | Q4_K_M | 3.8GB |
| AssertSolver.Q4_1.gguf | Q4_1 | 3.95GB |
| AssertSolver.Q5_0.gguf | Q5_0 | 4.33GB |
| AssertSolver.Q5_K_S.gguf | Q5_K_S | 4.33GB |
| AssertSolver.Q5_K.gguf | Q5_K | 4.46GB |
| AssertSolver.Q5_K_M.gguf | Q5_K_M | 4.46GB |
| AssertSolver.Q5_1.gguf | Q5_1 | 4.72GB |
| AssertSolver.Q6_K.gguf | Q6_K | 5.15GB |
| AssertSolver.Q8_0.gguf | Q8_0 | 6.67GB |
language:
Use the code below to get started with the model.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "1412312anonymous/AssertSolver"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
prompt = "Tell me how to fix the bugs inside: `always(*) // Pretend that this * should be rst`"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs,
max_new_tokens=512,
do_sample=False,
top_k=50,
top_p=0.95,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0][len(inputs[0]) :], skip_special_tokens=True))