Gemma 4
https://huggingface.co/collections/google/gemma-4 submitted by /u/Namra_7 [link] [comments]
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Fastest QWEN Coder 80B Next
I just used the new Apex Quantization on QWEN Coder 80B Created an Important Matrix using Code examples This should be the fastest best at coding 80B Next Coder around It's what I'm using for STACKS! so I thought I would share with the community It's insanely fast and the size has been shrunk down to 54.1GB https://huggingface.co/stacksnathan/Qwen3-Coder-Next-80B-APEX-I-Quality-GGUF https://preview.redd.it/wu924fls1dtg1.png?width=890 format=png auto=webp s=0a060e6868a5b88eabc5baa7b1ef266e096d480e submitted by /u/StacksHosting [link] [comments]
Error While using langchain with huggingface models
from langchain_core.prompts import PromptTemplate from langchain_community.llms import HuggingFaceEndpoint import os os.environ[“HUGGINGFACEHUB_API_TOKEN”] = “hf_your_new_token_here” prompt = PromptTemplate( input_variables=[“product”], template=“What is a good name for a company that makes {product}?” ) llm = HuggingFaceEndpoint( repo_id=“mistralai/Mistral-7B-Instruct-v0.3”, temperature=0.7, timeout=300 ) chains = prompt | llm print(“LLM Initialized with Token!”) try: response = chains.invoke({“product”: “camera”}) print(“AI Suggestion:”, response) except Exception as e: print(f"Error details: {e}") when i run this i get Value error can anyone help me out? Its a basic prompt template and text gen code but still it doesnt work i used various models from Huggingface and its not working well
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Fine-tuned Gemma 4 E4B for structured JSON extraction from regulatory docs - 75% to 94% accuracy, notebook + 432 examples included
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Gemma 4 Uncensored (autoresearch results)
Gemma 4 Uncensored — all 4 models, MoE expert abliteration, automated research loop Released uncensored versions of all four Gemma 4 models. bf16 + GGUF for each. Collection : https://huggingface.co/collections/TrevorJS/gemma-4-uncensored-69d2885d6e4fc0581f492698 Code : https://github.com/TrevorS/gemma-4-abliteration Results Model Baseline After KL Div E2B (2.3B) 98% 0.4% 0.346 E4B (4.5B) 99% 0.7% 0.068 26B MoE 98% 0.7% 0.090 31B 100% 3.2% 0.124 Refusal rates from 686 prompts across 4 datasets (JailbreakBench, tulu-harmbench, NousResearch, mlabonne). Manually audited — most flagged refusals are actually the model complying with a disclaimer attached. 26B MoE Standard abliteration only touches dense layers, which gets you from 98% → 29% on the MoE. The remaining refusals are in the expert w
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