All tags
Model: "qwen-1.5"
not much happened today
llama-2-70b llama-2-7b mistral-7b qwen-1.5 llava microsoft mistral-ai ollama fine-tuning synthetic-data retrieval-augmented-generation embeddings hardware-optimization performance-benchmarks model-memory multimodality
The Reddit community /r/LocalLlama discusses fine-tuning and training LLMs, including tutorials and questions on training models with specific data like dictionaries and synthetic datasets with 25B+ tokens. Users explore retrieval-augmented generation (RAG) challenges with models like mistral-7b and embedding generation for EEG brain activity. Discussions include hardware optimization for running llama-2-70b locally under budget constraints, and performance benchmarks for qwen-1.5 models. There is interest in extending LLM capabilities, such as converting llama-2-7b into a vision-capable model like llava and improving model memory for longer context retention.
The Dissection of Smaug (72B)
smaug-72b qwen-1.0 qwen-1.5 gpt-4 mistral-7b miqumaid wizardlm_evol_instruct_v2_196k openhermes-2.5 abacus-ai hugging-face nous-research laion thebloke lm-studio intel nvidia elevenlabs fine-tuning model-merging quantization web-ui model-conversion hardware-setup privacy image-generation optical-character-recognition prompt-engineering bindureddy
Abacus AI launched Smaug 72B, a large finetune of Qwen 1.0, which remains unchallenged on the Hugging Face Open LLM Leaderboard despite skepticism from Nous Research. LAION introduced a local voice assistant model named Bud-E with a notable demo. The TheBloke Discord community discussed model performance trade-offs between large models like GPT-4 and smaller quantized models, fine-tuning techniques using datasets like WizardLM_evol_instruct_V2_196k and OpenHermes-2.5, and challenges in web UI development and model merging involving Mistral-7b and MiquMaid. The LM Studio Discord highlighted issues with model conversion from PyTorch to gguf, hardware setups involving Intel Xeon CPUs and Nvidia P40 GPUs, privacy concerns, and limitations in image generation and web UI availability.
Qwen 1.5 Released
qwen-1.5 mistral-7b sparsetral-16x7b-v2 bagel-7b-v0.4 deepseek-math-7b-instruct deepseek qwen mistral-ai hugging-face meta-ai-fair quantization token-context multilinguality retrieval-augmented-generation agent-planning code-generation sparse-moe model-merging fine-tuning direct-preference-optimization character-generation ascii-art kanji-generation vr retinal-resolution light-field-passthrough frozen-networks normalization-layers
Chinese AI models Yi, Deepseek, and Qwen are gaining attention for strong performance, with Qwen 1.5 offering up to 32k token context and compatibility with Hugging Face transformers and quantized models. The TheBloke Discord discussed topics like quantization of a 70B LLM, the introduction of the Sparse MoE model Sparsetral based on Mistral, debates on merging vs fine-tuning, and Direct Preference Optimization (DPO) for character generation. The Nous Research AI Discord covered challenges in Japanese Kanji generation, AI scams on social media, and Meta's VR headset prototypes showcased at SIGGRAPH 2023. Discussions also included fine-tuning frozen networks and new models like bagel-7b-v0.4, DeepSeek-Math-7b-instruct, and Sparsetral-16x7B-v2.