What are the best datasets for fine-tuning a code generation model?


The best datasets for fine-tuning a code generation model depend on your target programming languages, generation tasks (completion, translation, repair, etc.), and ethical/data-licensing considerations. Here are some of the most widely cited and respected datasets as of 2024:

---

**Open Datasets for Code Generation**

- **The Stack** (by BigCode)
  - Large, permissively-licensed collection of source code from GitHub, filtered for quality and legal safety.
  - Covers over 350 programming languages.
  - [Project page](https://huggingface.co/datasets/bigcode/the-stack)
- **CodeSearchNet**
  - Source code (functions) and natural language documentation/comments in six languages: Go, Java, JavaScript, PHP, Python, Ruby.
  - [Project page](https://github.com/github/CodeSearchNet)
- **BigQuery GitHub dataset**
  - A huge raw archive of GitHub public repository snapshots, mostly uncurated — be careful with duplication, obsolete code, and licenses.
  - [Docs](https://cloud.google.com/bigquery/public-data/github)
- **CodeParrot**
  - Python-focused dataset built from GitHub code, filtered for permissive licenses.
  - [Project page](https://huggingface.co/datasets/codeparrot/github-code)
- **MBPP (Mostly Basic Python Problems)**
  - Crowdsourced Python programming problems with solution/test pairs, for function synthesis.
  - [Project page](https://huggingface.co/datasets/mbpp)
- **HumanEval and HumanEval-X**
  - Hand-curated Python and multilingual code generation evaluation problems, used for benchmarking rather than training.
  - [Project page](https://github.com/openai/human-eval)
  - HumanEval-X extends to 8 languages.
- **APPS**
  - Automatic Programming Progress Standard: exam-like Python programming problems for function synthesis and reasoning.
  - [Project page](https://github.com/hendrycks/apps)

---

**Specialty and Task-Specific Datasets**

- **MultiPL-E**
  - Benchmark for code generation in over 20 languages.
- **CONCODE**
  - For code generation from natural language to code (Java).
- **Bugs2Fix/CommitGen**
  - For program repair/generation from buggy code and patches.

---

**Key Considerations**

- **Licensing:** Always filter for permissively licensed code (MIT, Apache, BSD, public domain) for commercial use. Avoid GPL or no-license content unless your application is fully open source and you’re comfortable with viral license obligations.
- **Quality/Filtering:** Raw code datasets need cleaning (remove minified, obfuscated, or low-quality code).
- **Task Alignment:** Use function/problem‑centric datasets (MBPP, HumanEval, APPS) for synthesis/benchmarking, and repository snapshots (The Stack, CodeSearchNet) for broader language modeling.

---

**Next Steps**

- Select your target tasks and languages.
- Start with "The Stack" or CodeSearchNet for general fine-tuning.
- Supplement with MBPP, APPS, HumanEval for function generation/synthesis.
- Filter and clean data for quality and licensing compliance.

If you share more about your specific use case (languages, tasks), I can recommend a more focused set!


Where can I find high-quality instruction-tuning datasets?


Here are several trusted sources for high-quality **instruction-tuning datasets** widely used for training and evaluating instruction-following models:

**1. Hugging Face Datasets Hub**
- The [Hugging Face Datasets](https://huggingface.co/datasets) platform hosts a wide range of instruction-tuning datasets. Search tags like `instruction-tuning`, `chat`, or `instruction-following` for relevant collections.
  - Example: [`databricks/databricks-dolly-15k`](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
  - Example: [`tatsu-lab/alpaca`](https://huggingface.co/datasets/tatsu-lab/alpaca)

**2. Popular Instruction-Tuning Datasets**
- **OpenAssistant/oasst1** (large crowdsourced multi-language instructions): [dataset link](https://huggingface.co/datasets/OpenAssistant/oasst1)
- **FLAN v2**: A massive collection from Google of diverse instruction datasets ([GitHub link](https://github.com/google-research/FLAN), [aggregated set on HF](https://huggingface.co/datasets/FLAN_collection))
- **ShareGPT**: Real user conversations with ChatGPT ([HF mirror](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered))
- **Self-Instruct**: Synthetic dataset via large model self-improvement ([dataset link](https://huggingface.co/datasets/yizhongw/self-instruct))

**3. Aggregated and Curated Collections**
- **OpenHermes**: [OpenHermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5)
- **Instruction-Tuning Data Collection by HuggingFace**: [awesome-instruction-datasets list](https://github.com/jerryjliu/awesome-instruction-datasets)

**Finding More**
- Search `[instruction-tuning language model dataset]` on [Papers With Code](https://paperswithcode.com/) or [arXiv.org](https://arxiv.org/list/cs.CL/recent) for latest releases.

**Tips**
- Always review the dataset’s license, data origin, and quality indicators before use.
- Many datasets are hosted in full or with samples on Hugging Face, ready for `datasets.load_dataset`.

Let me know if you’re looking for data for a specific language, use-case, or size!


What open-source datasets are available for training a chatbot?


There are many open-source datasets available for training chatbots, covering a range of domains such as open-domain conversation, customer support, task-oriented dialogue, and technical Q&A. Here’s a concise list of well-known options, each with a brief description:

**Open-Domain Chat/Dialogue:**
- **OpenSubtitles**
  Large collection of movie subtitles, suited for informal, conversational exchanges.
- **DailyDialog**
  High-quality multi-turn dialogues on daily situations, useful for training conversational AI.
- **Persona-Chat**
  Crowdsourced dataset where each dialogue partner adopts a persona (profile), promoting personality in conversation.
- **The Dialogue Natural Language Inference Dataset (DNLI)**
  Large set of dialogue utterance pairs labeled for entailment, helpful for response selection and understanding.
- **ConvAI2**
  Competition dataset based on Persona-Chat, curated for conversational agents.

**Technical/Knowledge Q&A:**
- **Stack Exchange Data Dump**
  Contains Q&A data from Stack Overflow, Ask Ubuntu, and other Stack Exchange sites for technical chatbots.
- **WikiQA**
  Questions and answers based on Wikipedia articles—great for fact-based chat.

**Customer Support / Task-Oriented:**
- **MultiWOZ**
  Large-scale, multi-domain, Wizard-of-Oz dialogue dataset for task-oriented bots (travel, booking, etc.).
- **DSTC (Dialog State Tracking Challenge) datasets**
  Multiple datasets from dialog system competitions, often focused on task completion.

**Specialized/Other:**
- **Twitter Customer Support**
  Public Twitter conversations between customers and brands, used for training customer-service chatbots.
- **Cornell Movie-Dialogs Corpus**
  Movie-character conversations for open-domain chatbot training.

**Multi-Language:**
- **CC100 (part of OSCAR Corpus)**
  Massive, multilingual dataset extracted from Common Crawl, useful for multilingual chatbots.

**Where to find them:**
Most of these datasets are available via:
- [Hugging Face Datasets](https://huggingface.co/datasets) (search by name)
- [Awesome-ChatGPT-Prompts/Datasets](https://github.com/f/awesome-chatgpt-prompts)
- [Papers With Code - Dialogue](https://paperswithcode.com/datasets?task=dialogue)

**Tip:**
Before using, always check the dataset license and ensure it aligns with your intended use (academic, commercial, etc.).

If you want details or direct links for any specific dataset or need alternates for a particular language/domain, let me know!


Best multilingual NLP datasets for production use


Here are several highly regarded multilingual NLP datasets suitable for **production use**. These datasets support a variety of NLP tasks, offer robust quality, and are commonly used in industry and research. For production-grade models, always verify license compatibility with your product.

---

**General Purpose & Benchmarks**

- **XTREME / XTREME-R**
  - *Tasks*: Classification, QA, NER, SENT, others
  - *Languages*: 40–50+
  - *Notes*: Covers a wide spectrum of tasks, used to benchmark multilingual models. [XTREME-R GitHub](https://github.com/google-research/xtreme)

- **SuperGLUE Multilingual / XGLUE**
  - *Tasks*: Multiple (classification, NLI, QA, etc.)
  - *Languages*: 19
  - *Notes*: Production-suited benchmarks, widely cited.

---

**Translation & Parallel Corpora**

- **OPUS** ([opus.nlpl.eu](https://opus.nlpl.eu))
  - *Tasks*: Translation, Alignment, many others
  - *Languages*: 100+
  - *Notes*: Aggregator for open parallel datasets (Europarl, OpenSubtitles, JW300, etc).

- **Flores-200**
  - *Tasks*: Machine translation, evaluation
  - *Languages*: 200
  - *Notes*: High-quality, balanced across language families.

- **WMT (Workshop on Machine Translation)**
  - *Tasks*: Machine translation, evaluation
  - *Languages*: 10–40+
  - *Notes*: Yearly, industry-standard evaluation set.

---

**Classification & NLI**

- **XNLI**
  - *Tasks*: Natural Language Inference
  - *Languages*: 15
  - *Notes*: Standard for cross-lingual NLI.

- **Amazon Reviews (Multilingual)**
  - *Tasks*: Sentiment, classification
  - *Languages*: 6+
  - *Notes*: Real-world product reviews; useful for robust classifiers.

- **Massive**
  - *Tasks*: Intent detection, slot filling
  - *Languages*: 51
  - *Notes*: Built for conversational AI; labeled utterances.

---

**QA & Reading Comprehension**

- **MLQA**
  - *Tasks*: Question Answering
  - *Languages*: 7
  - *Notes*: Wikipedia-based, well-suited for production QA development.

- **TyDi QA**
  - *Tasks*: Question Answering
  - *Languages*: 11
  - *Notes*: Diverse, typologically-rich, real questions.

---

**NER & POS**

- **WikiANN (PAN-X)**
  - *Tasks*: Name Entity Recognition
  - *Languages*: 282
  - *Notes*: Derived from Wikipedia, widely used for cross-lingual NER.

---

**Conversational/Dialogue**

- **MASSIVE, MultiATIS++**
  - *Tasks*: Slot filling, intent detection
  - *Languages*: MASSIVE (51), MultiATIS++ (9)
  - *Notes*: Robust for production conversational AI.

---

**Best Practices for Production Datasets**

- **License Check**: Confirm dataset licenses suit your deployment (esp. commercial products).
- **Domain Adaptation**: For best results, fine-tune on in-domain data if available.
- **Bias & Coverage**: Evaluate for source bias and language balance in your use case.

---

If you have a specific **application or task domain** in mind (e.g., chatbots, document classification, medical text), I can help recommend the most targeted datasets for it. Let me know your needs!


What datasets should I use for evaluating LLM reasoning?


Here are some of the best-known datasets for evaluating LLM (Large Language Model) reasoning abilities, depending on the kind of reasoning you want to assess:

**General and Multitask Reasoning**
- **BIG-Bench (Beyond the Imitation Game Benchmark):** A large benchmark suite with hundreds of tasks covering common sense, mathematical, symbolic, and logical reasoning.
- **MMLU (Massive Multitask Language Understanding):** Covers 57 tasks across various subjects to assess breadth and reasoning.
- **ARC (AI2 Reasoning Challenge):** Focuses on grade-school science requiring reasoning skills over pure fact recall.
- **GAOKAO-Bench:** Exam-style questions inspired by China’s Gaokao university entrance exam; tests complex, multi-step reasoning.

**Mathematical Reasoning**
- **GSM8K:** Grade school math problems for multi-step arithmetic and reasoning.
- **MATH Dataset:** Contains high-school competition-level math problems.
- **AQUA-RAT:** Quantitative reasoning with rationales (step-by-step math explanations).
- **MathQA:** Mathematics word problems and their solutions.

**Commonsense Reasoning**
- **CommonsenseQA:** Explores models’ ability to pick the most plausible answer among several commonsense choices.
- **OpenBookQA:** Requires the use of an “open book” of facts and reasoning for science questions.
- **WinoGrande:** Commonsense pronoun resolution (harder version of Winograd Schema Challenge).

**Logical and Symbolic Reasoning**
- **ReClor:** Logical reasoning on standardized exams (reading comprehension style).
- **LogiQA:** Focuses solely on logical reasoning in reading comprehension.

**Chain-of-Thought & Multi-step Reasoning**
- **StrategyQA:** Questions requiring implicit decomposition and multi-hop reasoning.
- **HotpotQA:** Multi-hop question answering over Wikipedia.

**Scientific Reasoning**
- **ARC-DA (AI2 Reasoning Challenge - Diagnostics):** Subset of ARC designed for diagnostics.
- **SciQ:** Multiple-choice science exam questions.

**Programming and Code Reasoning**
- **HumanEval:** Code generation and reasoning on programming tasks (Python-based).
- **MBPP (Mostly Basic Python Problems):** Includes simple code generation and reasoning.

---

**How to Choose:**
- For broad reasoning: start with **BIG-Bench**, **MMLU**, and **ARC**.
- For math: **GSM8K**, **MATH Dataset**.
- For commonsense: **CommonsenseQA**, **WinoGrande**.
- For logic: **ReClor**, **LogiQA**.
- For chain-of-thought: **StrategyQA**, **HotpotQA**.

Let me know your specific focus (math, logic, coding, common sense, etc.) and I can recommend a more targeted set or provide download links!


What's the best LLM for code generation right now?


As of April 2026, the best large language models (LLMs) for code generation are generally considered to be:

- **OpenAI GPT-5.4 Codex** (internal/early access as of early 2026)
  - Delivers strong code synthesis, multi-language support, and agentic coding abilities.
  - Integrated in platforms like Codex CLI and GitHub Copilot X.

- **Anthropic Claude 3.5 Opus**
  - Notable for strong reasoning and code understanding, with safe code generation.
  - Performs well in longer coding and system design tasks.

- **Google Gemini Ultra**
  - Impressive performance in multi-modal (text, code, images) tasks and robust code support.

**Choosing the best for your use case:**
- For most general code generation, OpenAI’s models (especially GPT-5.4 Codex) set the bar for accuracy, multi-step reasoning, and handling large contexts.
- For projects focused on safe AI or extended, thoughtful coding tasks, Claude 3.5 is highly regarded.
- Gemini Ultra shines if you need code understanding alongside other unstructured data (e.g., code linked to images or diagrams).

**Other Noteworthy Models:**
- **Meta Llama-3 Code** and **Cohere Coral** are open and gaining traction, but generally rank below the models listed above for raw code-gen power.

**Recommendation:**
If you have access, use **GPT-5.4 Codex** for the most reliable and advanced code generation.
For open-source or non-OpenAI options, **Llama-3 Code** is the best available.

Let me know if you want benchmarks, API usage tips, or deployment guidance for any of these!


Which model should I use for structured data extraction from text?


For **structured data extraction from text**, especially in 2024-2026, the top models and approaches depend on the complexity of your data, privacy needs, volume, and whether you need production-ready or prototyping solutions. Here’s a concise breakdown:

---

**Best Models for Structured Data Extraction**

- **OpenAI GPT-4 Turbo or GPT-4o**
  - Great for general-purpose extraction tasks.
  - Supports JSON, XML, or other structured formats via prompt engineering (functions/tool calls/structured output).
  - GPT-4o is faster and cheaper than Turbo, with improved structure awareness.
  - Use with `"response_format": {"type": "json_object"}` for guaranteed valid JSON output.

- **OpenAI GPT-3.5 Turbo**
  - Good for lighter workloads and quick prototyping.
  - Less robust for edge cases and complex schemas but suitable for many use cases.

- **Llama 2/3 (Meta) or Mistral (open source)**
  - Good for on-premise/private workloads.
  - May require custom finetuning and structured prompt patterns (JSON schema in prompt).
  - Tools like Haystack or LangChain help with orchestration/template management.

- **Google Gemini Pro or Anthropic Claude 3**
  - Excellent at following structured output instructions.
  - Useful if you already work in the Google/Anthropic ecosystem.

---

**How to Use for Extraction (Prompt Example)**

OpenAI API (with GPT-4o):
```json
POST https://api.openai.com/v1/chat/completions
{
  "model": "gpt-4o",
  "messages": [
    { "role": "system", "content": "Extract the following fields from user messages: name, date, location." },
    { "role": "user", "content": "Jane Smith will arrive on June 5, 2026 in Madrid." }
  ],
  "response_format": { "type": "json_object" }
}
```

**Result:**
```json
{
  "name": "Jane Smith",
  "date": "June 5, 2026",
  "location": "Madrid"
}
```

---

**Summary Table**

| Model            | Strengths                     | When to Use                |
|------------------|------------------------------|----------------------------|
| GPT-4o/Turbo     | Accuracy, JSON output, speed | Most general use cases     |
| GPT-3.5 Turbo    | Cheap, fast                  | Prototyping, lighter loads |
| Llama/Mistral    | Open, private                | On-premise, full control   |
| Gemini/Claude 3  | Strong JSON output           | Google/Anthropic users     |

---

**Tips**
- Use function calling or JSON response mode for bulletproof extraction.
- If using open-source models, rely on prompt templates and possibly some custom code for post-processing.
- For production, always validate the output against your schema.

Would you like implementation examples or recommendations for a specific language or platform?


Best multimodal model for analyzing images and text together


As of April 2026, the **best multimodal models** for analyzing images and text together are:

- **GPT-4o (OpenAI)**
  - Industry leader for high accuracy tex+image reasoning
  - Supports document/image question answering, OCR, and complex joint understanding
  - Powers ChatGPT and many professional AI applications
- **Gemini 1.5 Pro (Google DeepMind)**
  - Excels in general multimodal understanding, including code, images, and video
  - Strong for technical and creative multimodal analysis
- **Claude 3 Opus/Sonnet (Anthropic)**
  - Very strong at image+text logic, document QA, and safety-critical use cases
- **LLaVA-Next, Qwen-VL-Plus (Open Source)**
  - Best for local/private/self-hosted tasks
  - Not as strong as GPT-4o or Gemini, but highly flexible

**Recommendations:**
- For maximum accuracy and reliability: **GPT-4o** or **Gemini 1.5 Pro**
- For open-source or self-hosted needs: **LLaVA-Next** (aligned to GPT-4v level on benchmarks), or **Qwen-VL-Plus**
- For researcher and developer flexibility: Try multiple via hosted APIs or platform-specific integrations (OpenAI, Google, Anthropic).

**Key considerations:**
- **Task type:** Some models better at vision-language (e.g., OCR, document QA), others at free-form reasoning.
- **Cost/Privacy:** OpenAI, Google, Anthropic are closed-source/cloud; LLaVA/Qwen are local.
- **Integration:** Check API docs for specific capabilities and limits.

Would you like a code sample showing how to use one of these models, or a comparison chart for your specific use case?


What's the best embedding model for semantic search?


The **best embedding model for semantic search** depends on your specific requirements (speed, accuracy, language support, and cost). Here’s a high-level breakdown of the current top options as of 2024:

---
**OpenAI Models**
- **text-embedding-3-large**
  - *Best overall performance, multi-language, very high accuracy for semantic search and retrieval tasks*
  - Strong support and ecosystem, but paid API only
  - [Documentation](https://platform.openai.com/docs/guides/embeddings)

- **text-embedding-3-small**
  - *Very fast and cost-effective, slightly lower performance than `-large` but beats most open-source options*
  - Great for large-scale or cost-sensitive use cases

---
**Open Source LLM Embedding Models**
- **BAAI/bge-large-en / bge-large / bge-base (English/Multilingual)**
  - *State-of-the-art open source options, especially `bge-large` for English*
  - [bge-m3](https://huggingface.co/BAAI/bge-m3) is top-ranked for multilingual
  - Hugging Face, free to run, competitive with OpenAI 002 and 3-small

- **sentence-transformers/all-mpnet-base-v2**
  - *Solid general-purpose model, used widely in open-source projects*
  - Slightly behind `bge` and OpenAI 003/003 models, but very efficient

- **GTE-large / GTE-base**
  - *Google’s open source embedding family, strong all-rounder*

---
**Other Notable Models**
- **Jina-embeddings-v2-base-en / multilingual**
  - *Competitive with above models for retrieval, especially in multi-language settings*

---

**Summary Table**

| Model                         | Accuracy | Langs | Cost/Ease   | API?      |
|-------------------------------|----------|-------|-------------|-----------|
| text-embedding-3-large (OAI)  | ★★★★☆    | ★★★★☆| $$$         | Yes       |
| BAAI/bge-m3 (open)            | ★★★★☆    | ★★★★☆| Free        | No        |
| bge-large-en / bge-large (open)| ★★★★☆   | ★★★★☆| Free        | No        |
| all-mpnet-base-v2 (open)      | ★★★☆☆    | ★★☆☆☆| Free        | No        |
| Jina v2-multilingual          | ★★★★☆    | ★★★★☆| Free        | No        |
| text-embedding-3-small (OAI)  | ★★★★☆    | ★★★★☆| $$          | Yes       |

---

**Recommendations**
- **Highest quality, commercial:** Use `text-embedding-3-large` from OpenAI.
- **Free/Open-Source:** Use `BAAI/bge-m3` for multilingual, or `bge-large-en` for English-only.
- **Fast/cheap, still high quality:** Try `text-embedding-3-small` or `all-mpnet-base-v2`.

Let me know your use case (languages, runtime limits, API/self-host), and I can give a more specific recommendation or code sample!


Which LLM has the best long context window for processing large documents?


As of April 2026, here are the leading large language models (LLMs) with the best long context windows—ideal for processing large documents:

**Top LLMs by Maximum Context Window Size**

- **Anthropic Claude 3 Opus:**
  - **Supported context window:** Up to 200,000 tokens.
  - **Notes:** One of the very largest, strong real-world performance at long length.

- **Google Gemini 1.5 Pro:**
  - **Supported context window:** Up to 1 million tokens (1M tokens) in preview/beta.
  - **Notes:** Largest context window of any public/preview LLM as of 2026. Designed for massive document/chat analysis, but beta access may be required.

- **OpenAI GPT-4 Turbo (API version):**
  - **Supported context window:** Up to 128,000 tokens.
  - **Notes:** Reliable, broad commercial availability, but context smaller than Gemini/Claude.

- **OpenAI GPT-5.4 (current flagship):**
  - **Supported context window:** Up to **256,000 tokens** (for gpt-5.4-preview endpoint); varies by endpoint and preview access.
  - **Notes:** Strong high-end performance, released March 2026. Broad access still rolling out.

**Summary Table**

| Model                       | Max Context (Tokens) | Availability              |
|-----------------------------|---------------------|--------------------------|
| Google Gemini 1.5 Pro       | 1,000,000           | Preview/Beta (waitlist)  |
| Anthropic Claude 3 Opus     | 200,000             | Public                   |
| OpenAI GPT-5.4 (preview)    | 256,000             | Rolling out              |
| OpenAI GPT-4 Turbo          | 128,000             | Public                   |

**Notes:**
- **Gemini 1.5 Pro** and **Claude 3 Opus** are leaders for extremely large context windows (hundreds of thousands to millions of tokens).
- **OpenAI**’s latest GPT-5.4 preview models are competitive and ship with advanced reasoning features.
- The best model also depends on your task: some models may lose quality/relevance over truly massive contexts, and pricing/latency can differ.

**Recommendation:**
For the **largest context length** and “whole-book” analysis, try **Google Gemini 1.5 Pro** (if you have access), otherwise use **Anthropic Claude 3 Opus** or **OpenAI GPT-5.4** (256k context on preview endpoints).

Would you like more about specific models, pricing, or how to use large context in practice?


What's the best open-source alternative to GPT-4 I can self-host?


As of early 2024, the best open-source alternatives to GPT-4 that you can self-host are advanced LLMs from the **Llama 2** and **Mixtral** families, and the emerging **Qwen** and **DeepSeek** models. Here are the leading options, considering quality, openness, and self-hosting viability:

---

**1. Llama 2 & Llama 2-Chat (Meta)**
- **Open weights**: Yes (with restrictions)
- **Best models:** Llama 2-70B and Llama 2-13B
- **Quality:** Among the highest for open models; Llama 2-70B-chat rivals GPT-3.5-level performance and is often the first choice for developers.
- **Requirements:** Powerful GPU(s); 70B runs best on 2–4x 48–80GB GPUs (or using quantized versions on a single 24/32GB GPU).
- [Meta Llama 2 — Official Page](https://ai.meta.com/llama/)

---

**2. Mixtral 8x7B (Mistral AI)**
- **Architecture:** Mixture-of-Experts; high efficiency and strong performance.
- **Open weights:** Yes (Apache 2.0)
- **Quality:** On par with or better than Llama 2-70B on many benchmarks while being much more efficient (can run on a single 24–48GB GPU when quantized).
- **Quick adoption:** Quickly became a favorite for self-hosting; extremely popular in the open-source LLM community.
- [Mixtral 8x7B — HuggingFace Model Card](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)

---

**3. Qwen (Alibaba) & DeepSeek LLM**
- **Qwen 72B/110B** and **DeepSeek 67B/67B-Chat** are strong open models from China.
- **Qwen 72B** is one of the closest open-source models to GPT-4 on certain tasks, available for commercial use.
- [Qwen on Hugging Face](https://huggingface.co/Qwen)
- [DeepSeek LLM](https://huggingface.co/deepseek-ai)

---

**4. Mistral Medium / Large**
- **Mistral AI** has released Mistral-medium (Mixture-of-Experts, strong chat and instruction following; Apache 2.0).
- [Mistral Medium on Hugging Face](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)

---

**Summary Table**

| Model                   | License    | Size    | Best for           | GPT-4 Quality? |
|-------------------------|------------|---------|--------------------|----------------|
| Llama 2-70B             | Custom     | 70B     | General LLM use    | Near-GPT-3.5   |
| Mixtral 8x7B            | Apache-2.0 | 46.7B*  | Efficiency, chat   | Near-GPT-3.5   |
| Qwen 72B/110B           | Apache-2.0 | 72B/110B| Advanced LLM tasks | Approaching    |
| DeepSeek LLM 67B        | MIT        | 67B     | General LLM use    | Approaching    |

\*MoE model, so inference is much more memory efficient.

---

**Recommendation**

- If **max quality** and **broad support** matter: Try **Mixtral 8x7B** or **Llama 2-70B**.
- If **GPU/RAM is limited**: Use quantized versions (e.g., GGUF/llama.cpp, vLLM, or Ollama).
- For the **most "GPT-4-like" accuracy**, try the largest Qwen or Deepseek models, but expect high memory/GPU needs.

---

**How to Run**
- Use self-hosting tools like [`text-generation-webui`](https://github.com/oobabooga/text-generation-webui), [`lmstudio`](https://lmstudio.ai/), [`llama.cpp`](https://github.com/ggerganov/llama.cpp), or [`ollama`](https://ollama.com/).

Would you like step-by-step guidance to self-host any of these models, or recommendations for specific hardware/software stacks?


Best open-source model for code completion under 10B parameters


As of early 2024, the best open-source code completion models under 10B parameters are:

**1. CodeGemma-7B**
- From Google, trained specifically for code tasks.
- Excellent performance on code completion and generation.
- Available on Hugging Face: `google/codegemma-7b`
- Python, C, C++, Java, and several other languages.

**2. StarCoder-7B**
- Developed by BigCode (Hugging Face + ServiceNow).
- StarCoder-7B is strong for multi-language code completion and understanding.
- Well-documented, easy to use with the Transformers library: `bigcode/starcoderbase-7b`
- Licensed for commercial use.

**3. DeepSeek-Coder 6.7B**
- Trains on a diverse set of programming languages.
- Good for code synthesis, completion, and infilling.
- Model: `deepseek-ai/deepseek-coder-6.7b-instruct`
- Open, fast, and efficient.

**4. Llama-2 Code 7B (by Meta)**
- Llama-2 7B models fine-tuned for code (via community projects like CodeLlama-7B).
- Great quality for Python, C, and other languages.
- Model: `codellama/CodeLlama-7b`

---

**Summary Table**

| Model               | Parameters | License | Key Languages           | Notable Strengths         |
|----------------------|-----------|---------|------------------------|---------------------------|
| CodeGemma-7B         | 7B        | Apache 2| Python, C, C++, Java   | Accuracy, new, multi-task |
| StarCoder-7B         | 7B        | BigCode | Many (incl. Python)    | Multi-language, permissive|
| DeepSeek-Coder 6.7B  | 6.7B      | MIT     | Many                   | Good synthesis/infilling  |
| CodeLlama-7B         | 7B        | Community| Python, C, C++         | Popular, many variants    |

**Recommendation:**
- For general code completion and top open licensing, **CodeGemma-7B** and **StarCoder-7B** are the safest choices as of 2024.
- If you want best infilling support, **DeepSeek-Coder 6.7B** is great.

Let me know if you want example setup usage or comparisons on a particular language!


Best open-source vision-language model for document understanding


Here’s a concise, expert-grounded snapshot of the **best open-source vision-language models** for document understanding (as of early 2024):

---

**Best Open-Source Vision-Language Models for Document Understanding**

- **LLaVA (Large Language and Vision Assistant)**
  - **Highlights:** LLaVA v1.5 and v1.6 support document images (diagrams, screenshots, PDFs).
  - **Strengths:** Multimodal vision encoder + LLM (e.g., LLaMA), strong for OCR-free extraction, visual Q&A, and form/question answering.
  - **Repo:** [llava-hf/llava-v1.5](https://huggingface.co/llava-hf/llava-v1.5-13b)
  - **Demo:** [LLaVA online](https://llava-vl.github.io/)

- **Donut (Document Understanding Transformer)**
  - **Highlights:** Fully OCR-free model for structured document parsing (forms, receipts, invoices, etc.)
  - **Strengths:** Trained for key-value extraction and information retrieval from document layouts.
  - **Repo:** [naver-clova-ix/donut](https://github.com/clovaai/donut)
  - **HF Models:** [See models](https://huggingface.co/models?sort=downloads&search=donut)

- **LayoutLMv3**
  - **Highlights:** Combines visual, layout, and text features.
  - **Strengths:** Excellent for receipts, invoices, contracts, and complex docs with spatial structure; widely used in industry.
  - **Repo:** [microsoft/layoutlmv3](https://github.com/microsoft/unilm/tree/master/layoutlm)
  - **HF Models:** [See models](https://huggingface.co/models?search=layoutlmv3)

- **Pix2Struct**
  - **Highlights:** Vision-language model for structured text in images (charts, tables, web pages, docs).
  - **Strengths:** Chrome browser screenshots/docs to structured answers, supports question answering and information extraction.
  - **HF Models:** [Pix2Struct](https://huggingface.co/google/pix2struct-base)

---

## **How to Choose**
- If you want **multimodal chat and flexible visual Q&A**, try **LLaVA**.
- If your focus is on **extracting fields from structured docs** (forms, invoices), try **Donut** or **LayoutLMv3**.
- For **complex layouts or web-like docs**, consider **Pix2Struct**.

**All of these models are open-source, actively maintained, and available via Hugging Face.**

If you want more focused guidance (e.g., for a specific document type or workflow), let me know your use case!


What open-source LLMs can I run on a single GPU?


Here’s a quick overview of popular open-source LLMs that you can run on a single GPU, especially with consumer-class VRAM (24GB or less):

---

**Mainstream Options (Actively Maintained)**

- **Llama-2 (Meta)**
  - 7B and 13B models run well on a single 16–24GB GPU (quantized/int4–int8 versions possible for less VRAM)
  - Good quality, strong support, many variants (chat, code)

- **Mistral (Mistral AI)**
  - 7B and 8x7B (Mixtral) models fit on single GPUs (especially quantized)
  - Excellent performance per size; strong English capabilities

- **Phi-2/3 (Microsoft)**
  - Phi-2 is 2.7B, Phi-3 is 3.8B–14B range (Phi-3 Mini and Small models)
  - Efficient, very good language and coding performance for their size

- **Qwen (Alibaba)**
  - Qwen-1.5 7B–14B suitable for single GPU; good on English & Chinese
  - Qwen2 series is also emerging, still fits midrange VRAM

- **Gemma (Google)**
  - Gemma 2B/7B models, efficient and high-quality, permissive license

- **StableLM (Stability AI)**
  - Early versions up to 7B, with newer variants in development

---

**Hardware & Quantization Notes**

- **VRAM Required (rounded):**
  - 7B dense (fp16): 14–16GB+
  - 13B dense (fp16): 24GB (bare minimum)
  - Int4 quantization: Many 13B/33B–34B models can run on 12–16GB GPUs with less accuracy loss
- **Popular inference libraries:** `llama.cpp`, `text-generation-webui`, `vLLM`, `Ollama`
- **Consumer GPUs:** RTX 3060/3070 (8–12GB), 3080/3090/4070+ (16–24GB) can handle most 7B/13B models

---

**Getting Started/ Good Choices by VRAM**

- **6–8GB:** Phi-2, Qwen 1.8B, TinyLlama, Mistral 7B quantized
- **12GB:** Llama-2 7B, Mistral 7B, Gemma 7B quantized, Phi-3 Mini
- **16–24GB:** Llama-2 13B, Mixtral 8x7B quantized, Qwen 14B quantized

---

**Popular Hubs for Models**

- [Hugging Face Model Hub](https://huggingface.co/models?pipeline_tag=text-generation)
- [llama.cpp-compatible models](https://github.com/ggerganov/llama.cpp/blob/master/docs/models/MODELS.md)
- [Ollama library models](https://ollama.com/library)

---

**Summary Table**

| Model           | Size  | VRAM (fp16) | Languages      | License   |
|-----------------|-------|-------------|---------------|-----------|
| Llama-2         | 7B/13B| 16–24GB     | Multi         | Permissive|
| Mistral         | 7B    | 8–16GB      | English       | Apache 2  |
| Phi-2/3         | 2.7–14B| 6–16GB     | English, Code | MIT       |
| Qwen/Qwen2      | 7B–14B| 8–16GB      | Multilingual  | Apache 2  |
| Gemma           | 2B/7B | 4–12GB      | English       | Apache 2  |

---

**Tip:** For most consumer cards (8–24GB), 7B–13B quantized models offer the best balance of performance and capability. Try `llama.cpp` or `Ollama` for the simplest setup.

If you’re interested in a specific domain (code, multilingual, etc.), or want a getting-started command for your hardware, let me know!


Which open-source model is best for function calling and tool use?


The **best open-source model for function calling and tool use** as of early 2024 is generally considered to be **Meta’s [Code Llama-70B-Instruct](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)**, specifically with fine-tuning oriented toward tool use/function-calling tasks—such as **Code Llama-70B-Python** or similar variants. However, your choice depends on your requirements (e.g., language, speed, infrastructure, licensing).

Here’s a quick breakdown of the **top contenders** and their characteristics:

---

**1. Code Llama-70B-Instruct**
- **Strengths:** Excellent at code, parsing structured output, and following tool-calling-style formats.
- **Function Calling:** Handles structured prompts and output (e.g., JSON, function signatures) reliably.
- **Cons:** Requires a lot of memory (multi-GPU or high-end single GPU setups).

**2. Mixtral-8x7B (by Mistral)**
- **Strengths:** Faster and more memory-efficient than single large models. Good structured output, strong few-shot in tool use contexts.
- **Function Calling:** Handles tool-formatted tasks well, especially with proper prompt design (e.g., JSON schema, instructions).
- **Cons:** Slightly lower raw precision than 70B models, but competitively close.

**3. Deepseek-VL or Deepseek-Coder**
- **Strengths:** Optimized for programming and toolchains, competitive with Llama and Mistral families.
- **Function Calling:** Good at structured templates; may need prompt tuning.
- **Cons:** Slightly newer/less tested in the wild vs. Llama/Mixtral.

**4. Llama-3 (8B, 70B)**
- **Strengths:** Excellent few-shot tool use, very consistent output on syntactic tasks.
- **Function Calling:** Strong format fidelity, especially the 70B version.
- **Cons:** Still emerging in open-source applications, may require custom fine-tuning for advanced tool use.

**5. Zephyr (by HuggingFace), OpenChat, and others**
- **Strengths:** Lightweight, community-fine-tuned for tool use (especially Zephyr for OpenAI-style function calling).
- **Function Calling:** Good at lower resource costs, often designed for API-like output.
- **Cons:** Raw reasoning usually not as strong as Llama-3/CodeLlama-70B.

---

### **Summary Table**

| Model                  | Tool/Function Call Quality | Resource Req. | Notes                 |
|------------------------|---------------------------|---------------|-----------------------|
| Code Llama-70B-Instruct| ⭐⭐⭐⭐⭐                    | High          | Best for precision    |
| Mixtral-8x7B           | ⭐⭐⭐⭐                     | Moderate      | Efficient, flexible   |
| Llama-3 70B            | ⭐⭐⭐⭐⭐                    | High          | State-of-the-art      |
| Deepseek-family        | ⭐⭐⭐⭐                     | Moderate      | Specialized for code  |
| Zephyr / OpenChat      | ⭐⭐⭐                      | Low           | Good for small setups |

---

### **Best Practice**
- For **maximum accuracy** and tool-calling robustness:
  Use **Llama-3-70B**, **Code Llama-70B-Instruct**, or **Mixtral-8x7B** with a prompt template structured for function calling (e.g., JSON schema, OpenAI function-calling format).
- For **lighter infrastructure**:
  Try Zephyr, Deepseek-Coder, or Llama-3-8B.

> **Tip:** Community tool-use benchmarks (such as **OpenFunction** or **lm-eval-harness tool-use tasks**) are good references for latest results.

Do you want exact prompt templates or setup tips for function calling with these models?


How do I share a fine-tuned model with my team?


To share a fine-tuned model with your team, you typically have these reliable options:

- **Upload to Hugging Face Hub**
  - Most common for transformer-based models.
  - Push your fine-tuned model (and associated files like tokenizer, config, etc.) to the [Hugging Face Model Hub](https://huggingface.co/docs/hub/models-uploading).
  - Set permissions appropriately (private, organization, or public).
  - Team members can clone or load the model using `from_pretrained`.
  - Example for upload:
    ```
    transformers-cli login  # Or use `huggingface-cli login`
    from transformers import AutoModelForXXX, AutoTokenizer

    model.save_pretrained("my-model-dir")
    tokenizer.save_pretrained("my-model-dir")

    # In terminal
    huggingface-cli repo create my-org/my-model
    git lfs install
    cd my-model-dir
    git init
    git remote add origin https://huggingface.co/my-org/my-model
    git add .
    git commit -m "Upload fine-tuned model"
    git push origin main
    ```
  - Teammates can then load the model with:
    ```python
    from transformers import AutoModelForXXX
    model = AutoModelForXXX.from_pretrained("my-org/my-model")
    ```

- **Share via Cloud Storage or Internal Drive**
  - Save model and tokenizer locally (`model.save_pretrained`, `tokenizer.save_pretrained`).
  - Upload the model directory to a shared drive (Google Drive, Dropbox, company storage).
  - Teammates download and load the model using `from_pretrained("downloaded-dir/")`.

- **Organization Repos on Hugging Face Hub**
  - Create an **organization** on Hugging Face, invite your teammates, and upload the model to that org.
  - More information: [Hugging Face Organizations](https://huggingface.co/organizations)

- **Export to ONNX or Other Format**
  - For easier deployment or compatibility, export to ONNX, TorchScript, etc. and share those artifacts.

**Tips:**
- Include a `README.md` with usage examples.
- Share all necessary files: model, tokenizer, config, special tokens.
- Set clear permissions and access controls for private or internal models.

Let me know if you need step-by-step guidance for a specific method or platform!

transformers
from transformers
AutoTokenizer


Best way to version and collaborate on ML datasets


Here’s a concise guide on **versioning and collaborating on ML datasets**, focusing on practicality and strong team workflows:

---

**Popular Options**
- **DVC (Data Version Control)**
  - Track large data files, models, and experiments like code with Git.
  - Store data locally, on shared drives, or cloud storages (S3, GCS, Azure).
  - Each version is linked to Git commits; easy collaboration, sharing, and rollback.
  - CLI and UI tools; integrates with CI/CD and experiment tracking.

- **LakeFS**
  - `git`-like branching and versioning for data lakes (S3, GCS).
  - Enables atomic data changes, reproducibility, and isolated experimental “branches” of data.
  - Great for larger, cloud-native data teams.

- **Weights & Biases Artifacts**
  - Integrates directly with W&B experiment tracking.
  - Allows you to log, version, and compare datasets, models, etc.
  - Good for teams already using W&B.

- **Git LFS**
  - Extension of Git, for large files.
  - Limited compared to DVC or LakeFS, but useful for smaller data or quick collaboration.

---

**Best Practices**
- **Dataset as Code:** Treat datasets just like source code. Keep metadata (README, preprocessing scripts) version-controlled.
- **Use Cloud Storage:** Store large files in cloud (S3, GCS); reference via pointers in your versioning tool.
- **Define Versioning Strategy:**
  - Semantic versions (`v1.0`, `v1.1`) or timestamped.
  - Track both raw and processed data.
- **Collaborative Workflows:**
  - Use branches for experimental changes (e.g., “feature/add-balance-sampling”).
  - Pull requests + reviews for merges, just like with code.
  - Automate validation (e.g., schema checks) in CI/CD.
- **Documentation:**
  - Keep a changelog for each dataset version.
  - Document data source, preprocessing, and any assumptions.
- **Access Control:**
  - Ensure sensitive data is properly controlled in cloud storage.

---

**Quick Comparison Table**

| Tool         | Pros                          | Cons                       | Scale      |
| ------------ | ---------------------------- | -------------------------- | ---------- |
| DVC          | Easy Git integration, mature  | Learning curve, extra tools| Small–large|
| LakeFS       | Data lake scale, branching    | Requires infra, cloud-only | Large      |
| W&B Artifacts| Built for ML, online UI      | Tied to W&B, cloud-costs   | Small–large|
| Git LFS      | Simple, Git-native           | Not for big data           | Very small |

---

**General Recommendation**

- **Small/medium teams** & code-like workflows: **DVC** is usually the best starting point.
- **Data lake scale / complex branching**: **LakeFS**.
- **Already on W&B**: Use **Artifacts**.
- Add strong **docs** and integrate with your Git/CI flow.

Let me know if you want concrete setup steps for any tool or help integrating with your current workflow!


How do I contribute a dataset to the open-source community?


To contribute a dataset to the open-source community, follow these **essential steps**:

**1. Prepare the Dataset**
- Ensure data is clean, well-organized, and free from sensitive/private info.
- Convert files to widely accepted formats (e.g., `.csv`, `.json`, `.parquet`, images as `.jpg`/`.png`, audio as `.wav`).

**2. Add Documentation**
- Create a clear `README.md` explaining:
  - What’s in the dataset (columns/features, size, usage).
  - Source of the data.
  - How it was collected/processed.
  - Terms of use/license.
  - Example usage code (optional but helpful).

**3. Choose an Open License**
- Pick a license so others know how they can use the data.
  - Examples: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/), [ODC-BY](https://opendatacommons.org/licenses/by/).
- Add a `LICENSE` file or include license info in your README.

**4. Share the Dataset**
- For small/medium datasets (up to a few GB):
  - [GitHub](https://github.com/): Create a repo, push your data and docs.
  - [Hugging Face Hub](https://huggingface.co/datasets): Free uploads, easy download/use from code.
  - [Zenodo](https://zenodo.org/): For scientific data; provides DOIs.
- For large datasets:
  - Upload to cloud storage (Google Drive, AWS S3, Azure Blob).
  - Share links in a GitHub repo or via [DataHub.io](https://datahub.io/).

**5. Announce and Maintain**
- Share on social media, forums (Reddit, Twitter/X, Open Data Stack Exchange).
- Maintain your data: respond to issues, update if errors are found.

---

**Platforms to Consider:**
- [Hugging Face Datasets Hub](https://huggingface.co/docs/hub/datasets-upload)
- [Kaggle Datasets](https://www.kaggle.com/datasets) (for ML/data science)
- [Zenodo](https://zenodo.org/)
- [GitHub](https://github.com/) (general purpose)

Would you like step-by-step guidance for a specific platform (e.g., Hugging Face, GitHub, Kaggle)? If you have a dataset ready, I can walk you through the contribution process!


What platforms support collaborative model training?


Here’s a list of prominent platforms that support **collaborative model training** (where multiple users or organizations can jointly contribute to training, fine-tuning, or data curation for machine learning models):

**Open Source Platforms**
- **OpenMined / PySyft:** Focuses on privacy-preserving, federated, and collaborative AI—with tools for hosting secure, multi-party machine learning.
- **Flower:** A framework for federated learning at scale; supports collaborative and decentralized model training.
- **Hivemind:** Framework for decentralized, peer-to-peer collaborative model training—originally developed by EleutherAI.
- **FedML:** Platform for federated and collaborative learning, supports cross-device and cross-silo collaboration.

**Commercial & Research-Supported**
- **Weights & Biases Teams:** Supports collaborative experiment tracking and can be paired with distributed training jobs; multiple team members can contribute to training runs.
- **Hugging Face Hub / Spaces:** Allows communities to co-develop, train, and fine-tune models with collaborative version control and sharing.
- **Google Colab Teams / Shared Notebooks:** Notebooks can be collaboratively edited and used for distributed training jobs.
- **AWS SageMaker Studio / Teams:** Enables collaboration on cloud-based ML projects, including distributed model training.

**Federated & Privacy-Focused Collaboration**
- **Google Federated Learning Framework (TensorFlow Federated):** For collaborative training across devices or orgs, with privacy guarantees.
- **OpenFL (by Intel):** Open Federated Learning platform for privacy-preserving, multi-party collaboration.

**Notable Community/Grassroots Projects**
- **EleutherAI / Open LLM efforts:** Community-run distributed training on volunteer compute.
- **BigScience (hosted by Hugging Face):** Large-scale, international collaborative model-building projects (e.g., BLOOM).

---

**Typical Capabilities**
- Multiple users can join training runs, contribute data, or supply compute.
- Version control and rights management for training artifacts.
- Experiment/metric tracking, dataset and model sharing, and joint fine-tuning.

If you have a specific use case or workflow in mind (e.g., privacy, scale, type of model, open vs. closed source), let me know and I can refine the list with more focused recommendations!


How do I set up access controls for shared model repositories?


To set up access controls for shared model repositories, you typically need to configure **who can view, modify, or manage the repository and its contents**. The process depends on the platform or system you’re using to host your model repositories (e.g., Hugging Face Hub, private Git server, internal platform). Here’s a general guide, with specifics for common scenarios:

---

**General Steps for Setting Up Access Controls**

- **1. Choose Access Model**
  Decide between public, private, or internal (restricted) visibility for the repository.

- **2. Define User Roles/Permissions**
  Common roles include:
  - Owner/Admin: Full control
  - Maintainer/Write: Push updates, edit config
  - Read-only/Viewer: Download and use, but not edit

- **3. Add/Invite Users or Teams**
  Use emails/usernames or groups to grant access.

- **4. Set Fine-Grained Controls**
  Restrict specific actions (e.g., upload models, delete, manage secrets), if platform allows.

- **5. Review and Audit**
  Regularly check access lists and permissions for correctness and security.

---

**Example: Hugging Face Hub**

- **Making a repo private:**
  - Go to the model repo page
  - Click "Settings"
  - Under "Repository visibility," set to **Private**
- **Managing users:**
  - Under "Collaborators," invite users by username/email
  - Assign them as **Read**, **Write**, or **Admin**
- **Orgs & Teams:**
  - With an organization, create teams and set their specific permissions per repo

Reference: [Hugging Face Docs – Sharing and Access Control](https://huggingface.co/docs/hub/security)

---

**Example: Git-based Model Repo**

- **GitHub, GitLab, or self-hosted:**
  - Use the platform’s repository settings to set visibility (public/private)
  - Add "Collaborators" and assign roles (admin, write, read)
  - For fine-grained rules, use branch protection and CODEOWNERS files

---

**Advanced Controls**

- **Integrate with SSO (Single Sign-On) or LDAP for enterprise**
- **Use API tokens/secrets for automation with limited scopes**
- **Enable activity logging/auditing for compliance**

---

**Next Steps**

- Let me know which platform or solution you’re using (e.g., Hugging Face, custom server, GitHub) for more tailored instructions.
- If using a specific framework or tool, check if it has built-in RBAC (Role-Based Access Control) or permissions modules.
- Always verify access by testing with a user account before sharing widely.

Would you like step-by-step help for a specific platform?


How do I download a large dataset in parquet format programmatically?


To download a large dataset in **parquet** format programmatically, you typically use data processing libraries or HTTP clients in languages like Python. The steps and code depend on the source of the data (e.g., HTTP(S) URL, AWS S3, GCP, Azure, Hugging Face Datasets, etc.). Below are the most common scenarios and methods:

---

**1. Download from a Direct URL**

If the Parquet file is at a direct HTTP(S) link:

```python
import requests

url = "https://example.com/data/large-dataset.parquet"
output_path = "large-dataset.parquet"

with requests.get(url, stream=True) as r:
    r.raise_for_status()
    with open(output_path, 'wb') as f:
        for chunk in r.iter_content(chunk_size=8192):
            f.write(chunk)
```

- Uses streaming to handle large files efficiently.

---

**2. Download from AWS S3**

If your dataset is in AWS S3:

```python
import boto3

s3 = boto3.client('s3')
bucket = 'my-bucket'
key = 'path/to/my-dataset.parquet'
output_path = 'my-dataset.parquet'

s3.download_file(bucket, key, output_path)
```
- Make sure you have AWS credentials configured.

---

**3. Download and Stream/Read with Pandas and PyArrow**

You can load Parquet files directly into memory with [pandas](https://pandas.pydata.org/) and [pyarrow](https://arrow.apache.org/):

```python
import pandas as pd

# For a local Parquet file
df = pd.read_parquet("large-dataset.parquet")

# For a Parquet file at a URL (if supported; some HTTP sources may not support direct access)
df = pd.read_parquet("https://example.com/data/large-dataset.parquet")
```
*Note: Not all URLs support random access needed by Parquet, so this works mainly for files on disk, cloud storage with fsspec, or S3 with proper plugins.*

---

**4. Download from Hugging Face Datasets**

If the dataset is hosted on the Hugging Face Hub:

```python
from datasets import load_dataset

dataset = load_dataset("dataset_name", split="train")
dataset.to_parquet("large-dataset.parquet")
```

---

**Key Tips**
- For very large datasets, **streaming** or **chunked downloads** is crucial to avoid memory issues.
- Parquet files often need random access; pure HTTP can be tricky unless the server supports range requests.
- Consider using [`fsspec`](https://fsspec.io/), which integrates well with cloud storage providers:
    ```python
    import fsspec

    fs = fsspec.filesystem("https")  # or "s3", "gs", etc.
    with fs.open('https://example.com/data/large-dataset.parquet', 'rb') as f:
        # Use with pyarrow or pandas as needed
    ```

---

Do you want example code for a specific platform (cloud provider, website, or dataset source)? If you share your source, I can give you an exact snippet!

from datasets
load_dataset
hugging face datasets


Where can I search for pre-processed NLP datasets by task type?


You can search for pre-processed NLP datasets by task type on the [Hugging Face Datasets Hub](https://huggingface.co/datasets). Here’s how you can do it:

- **Browse by Task**:
  On the left sidebar of the [Hugging Face Datasets page](https://huggingface.co/datasets), you’ll find a "Task Categories" filter.
  - Click a category (e.g., Text Classification, Question Answering, Named Entity Recognition) to see datasets specifically prepared for that NLP task.
- **Search by Keyword**:
  Use the search bar at the top to enter keywords related to your task (e.g., "sentiment analysis", "translation").
- **Dataset Cards**:
  Each dataset page provides details about processing, intended task, splits, and features.
  Many have examples and links to relevant papers/notebooks.

**Popular alternatives (by task type):**
- [Papers With Code Datasets](https://paperswithcode.com/datasets): Filter by task and benchmarks.
- [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview): Browse by NLP task (see "Natural Language" section).

**Summary**:
The [Hugging Face Datasets Hub](https://huggingface.co/datasets) is the most comprehensive and up-to-date resource for pre-processed NLP datasets grouped by task.

Let me know if you want a direct link for a specific task or more details!


How do I stream a dataset that's too large to fit in memory?


To stream a dataset that's too large to fit in memory, you should **process it in batches or chunks**, reading and handling a small portion at a time. The approach depends on your data source and language. Here are common strategies:

---

**1. CSV, JSON, or Text Files**

- **Python Example (CSV):**
  ```python
  import csv

  with open('large_dataset.csv', newline='') as csvfile:
      reader = csv.DictReader(csvfile)
      for row in reader:
          # Process each row
          print(row)
  ```

- **Python Example (JSON Lines):**
  ```python
  import json

  with open('large_data.jsonl') as f:
      for line in f:
          data = json.loads(line)
          # Process each data entry
          print(data)
  ```

- **Shell Example:**
  ```sh
  while read line; do
    # process "$line"
    echo "$line"
  done < largefile.txt
  ```

---

**2. Datasets in Machine Learning**

- **PyTorch DataLoader:**
  ```python
  from torch.utils.data import DataLoader

  dataset = YourCustomDataset()
  loader = DataLoader(dataset, batch_size=64)
  for batch in loader:
      # Training logic with each batch
      pass
  ```

- **TensorFlow `tf.data.Dataset`:**
  ```python
  import tensorflow as tf

  dataset = tf.data.TFRecordDataset(filenames)
  dataset = dataset.batch(64)
  for batch in dataset:
      # Process each batch
      pass
  ```

---

**3. Databases or Remote Sources**

- **SQL Example (with LIMIT/OFFSET):**
  ```sql
  SELECT * FROM table LIMIT 1000 OFFSET 0;
  -- Repeat with increasing OFFSET to process in chunks
  ```

- **Python (with Cursor Iterator):**
  ```python
  import sqlite3

  conn = sqlite3.connect('large.db')
  cursor = conn.execute("SELECT * FROM huge_table")
  for row in cursor:
      # Process row
      pass
  ```

---

**Tips for Efficient Streaming**
- Avoid loading all data into memory; process and discard or persist results as you go.
- For files: Use generators/iterators rather than materializing lists.
- For tar/zip: Use libraries like `tarfile` or `zipfile` and process entries one-by-one.
- For large datasets: Consider libraries like [Dask](https://dask.org/) or [PyTables](https://www.pytables.org/) that handle out-of-core data.

---

If you have a **specific file format, library, or programming language** in mind, let me know and I can provide a more targeted example!


Best way to preview dataset contents before downloading


To preview the contents of a dataset before downloading, here are the most effective approaches, depending on your platform and the dataset host:

**1. Hugging Face Datasets**
- Use the Hugging Face Datasets library’s `load_dataset(..., streaming=True)` to stream and preview rows without downloading the entire dataset.
- Alternatively, use the dataset’s [Hugging Face Hub page](https://huggingface.co/datasets) — most have built-in web explorers to preview rows, columns, and metadata.

**Example using Python (Hugging Face Datasets):**
```python
from datasets import load_dataset

# Stream the dataset, just peek at a few rows
ds = load_dataset("imdb", split="train", streaming=True)
for i, row in enumerate(ds):
    print(row)
    if i == 4:  # Preview first 5 rows
        break
```

**2. Kaggle Datasets**
- Use the Kaggle web UI to preview CSV/JSON/XLS files directly.
- For CLI, use:
  ```bash
  kaggle datasets metadata <dataset-id>
  kaggle datasets list-files <dataset-id>
  ```
- But for row previews, UI is best.

**3. Data on S3, GCS, or HTTP**
- List files, or use tools like `pandas.read_csv()` with `nrows=5`, or `duckdb` SQL on web-hosted parquet/CSV.
- For large files, tools like `s3select` can help filter/preview on the cloud.

**General Tips**
- Always look for a “preview,” “explore,” or “sample” tab/option on the host’s website.
- For custom APIs, check if they provide sample endpoints or quick-lookup methods.
- Avoid downloading with `wget/curl` unless you truly need the full file.

Let me know your data source or platform for more specific code or steps!

from datasets
load_dataset
hugging face datasets
datasets library


How do I filter and download only a subset of a large dataset?


To filter and download only a subset of a large dataset, there are several approaches depending on where the dataset is hosted and what tools you’re using. Here’s how you can do this for the most common scenarios:

---

**If the dataset is on [Hugging Face Hub](https://huggingface.co/datasets):**

- **With `datasets` Library (Python):**
    ```python
    from datasets import load_dataset

    # Example: Download only a subset using a filter
    dataset = load_dataset("dataset_name", split="train")
    filtered = dataset.filter(lambda example: example['field'] == 'value')
    filtered.save_to_disk("filtered_subset")
    ```
    - Replace `"dataset_name"`, `"field"`, and `"value"` with your dataset and filter criteria.
    - This downloads only the matching rows and saves the filtered subset locally.

- **Column Filtering:**
    ```python
    filtered = dataset.remove_columns(['unwanted_column_1', 'unwanted_column_2'])
    ```

- **Use Streaming for Huge Datasets (avoid full download):**
    ```python
    dataset = load_dataset("dataset_name", split="train", streaming=True)
    subset = (x for x in dataset if x['field'] == 'value')
    for i, example in enumerate(subset):
        # process or save examples as needed
        if i == 1000:  # break after 1000 examples
            break
    ```

---

**If the dataset is a CSV/TSV/Parquet file accessible via URL:**

- **Download & Filter with Pandas (Python):**
    ```python
    import pandas as pd

    url = 'https://path/to/your.csv'
    chunk_size = 10_000
    filtered_rows = []

    for chunk in pd.read_csv(url, chunksize=chunk_size):
        filtered = chunk[chunk['field'] == 'value']
        filtered_rows.append(filtered)

    result = pd.concat(filtered_rows)
    result.to_csv('filtered_subset.csv', index=False)
    ```

- This downloads only in chunks (doesn’t load the full file into memory), filters, and saves the result.

---

**If you want to download only specific files from a large public folder/storage:**

- **With `wget` or `aria2c`:**
    - List the files you want in a text file and use:
        ```sh
        wget -i files_to_download.txt
        ```
    - For advanced filtering based on filename patterns, use `--accept` or regex in your script.

---

**Tips:**
- Always check the dataset documentation for filtering/download options.
- For very large datasets, use streaming or chunked processing to avoid memory issues.
- If you want more specifics, share the dataset source (e.g., Hugging Face link, Google Drive, AWS S3, etc.) and your filter criteria.

Would you like a concrete example for a particular dataset or filtering operation?


How do I upload a custom dataset for public use?


To upload a custom dataset for public use, the steps depend on the platform or tool you’re referring to. Since you’re working in a repo named `hf-devtools-dashboard`, I'll assume you're asking about Hugging Face Datasets, which is common in ML/dev tooling. Here’s how you can do it:

---

**Hugging Face – Upload a Custom Dataset**

- **1. Prepare your dataset**
  - Structure it as CSV, JSON, Parquet, or a valid dataset script.
  - Clean sensitive/private information, as it will be public.

- **2. Install/Update `huggingface_hub` and `datasets`**
  ```
  pip install --upgrade huggingface_hub datasets
  ```

- **3. (Optional) Log in to the Hub**
  ```
  huggingface-cli login
  # Enter your HF credentials or use a token from https://huggingface.co/settings/tokens
  ```

- **4. Load your dataset locally to check validity**
  ```python
  from datasets import load_dataset
  dataset = load_dataset("path/to/your/data")
  print(dataset)
  ```

- **5. Push the dataset to Hugging Face Hub**
  ```python
  from datasets import load_dataset
  dataset = load_dataset("path/to/your/data")
  dataset.push_to_hub("your-username/your-dataset-name")
  ```
  Or use the CLI:
  ```
  huggingface-cli upload dataset \
    path/to/your/data \
    --repo your-username/your-dataset-name \
    --private false
  ```

- **6. Verify & Share**
  - Go to `https://huggingface.co/datasets/your-username/your-dataset-name` and make sure it’s public.
  - Add a README with clear usage instructions and dataset details.

---

**Notes**
- You can set visibility to "public" during upload or afterwards in the Hub UI.
- If uploading via web UI: [Hugging Face Datasets new dataset page](https://huggingface.co/new-dataset).
- If you meant a different platform, let me know!

Would you like step-by-step commands tailored to your specific dataset or a different upload method?


Best way to host a private dataset for my organization


The best way to host a **private dataset** for your organization depends on your specific requirements such as data size, privacy needs, access control, tech stack, and collaboration demands. Here are the most common approaches and their key considerations:

---

**1. Cloud Storage Services**
- **Providers:** [AWS S3](https://aws.amazon.com/s3/), [Google Cloud Storage](https://cloud.google.com/storage), [Azure Blob Storage](https://azure.microsoft.com/en-us/products/storage/blobs/)
- **Features:** Fine-grained access control (IAM, signed URLs), seamless scaling, global accessibility, versioning.
- **Best for:** Most organizations who want security, auditability, and integration with ML/data tools.
- **Access patterns:** You can restrict access to your org or specific users using roles.

---

**2. ML/Data Platforms**
- **Platforms:** [Hugging Face Hub](https://huggingface.co/docs/hub/index), [Weights & Biases Artifacts](https://docs.wandb.ai/guides/artifacts), [DVC Remote](https://dvc.org/doc/user-guide/setup-remote), [Azure ML Datasets](https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets), [Databricks](https://www.databricks.com/)
- **Features:** Dataset versioning, sharing, programmatic download, access control, organization-level privacy, often audit logs.
- **Best for:** ML and data science teams wanting to collaborate and track versions.
- **Notable:** Hugging Face Hub supports private org datasets – you can control access down to individual users.

---

**3. On-Premises/Internal File Servers**
- **Options:** NFS, SMB, self-hosted Minio S3, internal web servers.
- **Features:** Full control, can be integrated with VPN, firewalls, internal auth.
- **Best for:** When data **cannot leave your network** (compliance, regulation), or cloud isn’t acceptable.
- **Considerations:** Requires IT maintenance, backups, and security hardening.

---

**4. Git-Based Solutions**
- **Usage:** For small datasets or metadata (under 100MB per file), private GitHub/GitLab repos, LFS, or DVC with a git remote.
- **Features:** Built-in user/org permissioning, easy for code+data projects.
- **Limitations:** Not suitable for large datasets.

---

**5. Databases / Data Warehouses**
- **Options:** [Snowflake](https://www.snowflake.com/), [BigQuery](https://cloud.google.com/bigquery/), [AWS Redshift](https://aws.amazon.com/redshift/)
- **Features:** For tabular data, granular security, advanced analytics, and SQL-based sharing.

---

**Recommendations**

- If you are working with **ML/AI datasets**, consider **Hugging Face Hub (private repo)** or **DVC with a private remote (S3/GCS/Minio)**.
- For **general files and easy management**, AWS S3 with bucket policies is a robust and flexible default.
- For **strictly internal or regulated data**, use on-prem storage with strong access controls and auditing.
- Always enforce encryption at rest and in transit, and monitor access logs for compliance.

**Pro Tip:**
Design access so teammates have least-privilege by default (only permitted users can download/upload), and automate offboarding.

---

If you tell me:
- Your dataset size/type
- Preferred workflow (cloud, on-prem, ML platform, etc.)
- If you need versioning, audit logs, APIs, etc.
- Current stack (AWS, Azure, Google, etc.)

…I can recommend an exact setup & provide step-by-step instructions!


How do I add a dataset card with proper documentation?


To add a **dataset card** with proper documentation (typically for datasets intended to be shared on Hugging Face or similar ML/data repositories), you should include a `README.md` file with structured sections describing the dataset and providing usage info. Here’s what you should do:

---

**How to Add a Dataset Card**

- **Create a new file** named `README.md` in the root of your dataset folder, or `dataset_card.md` if a specific naming convention is required (Hugging Face Datasets expects `README.md`).
- Use **Markdown** to format the card.
- Include the following recommended sections:

---

### Example: Dataset Card Structure (`README.md`)

```markdown
---
dataset: your-dataset-name
tags:
  - tag1
  - tag2
license: mit
---

# Dataset Name

## Dataset Description
A concise overview describing what the dataset contains, its source, and intended use cases.

## Supported Tasks and Leaderboards
- **Task 1**: e.g., text classification, image recognition.
- **Task 2**: e.g., summarization, object detection.

## Languages
List language(s) represented in the data.

## Dataset Structure

### Data Instances
Describe what a single data point looks like (show an example).

### Data Fields
- **Field 1**: Description.
- **Field 2**: Description.

## Data Splits
If data is split (train/validation/test), explain the splits and proportions.

## Dataset Creation

### Source Data
Describe where/how data was collected.

### Annotations
Explain who labeled/annotated, guidelines, etc.

### Personal and Sensitive Information
State whether the dataset contains any such data.

## Licensing Information
Clearly state the license type (MIT, Apache 2.0, CC-BY, etc).

## Citation Information
If people use your dataset, how should they cite it? Provide BibTeX or text citation.

## Additional Information
Any extra context, links to papers, or related resources.
```

---

**Tips:**

- Check Hugging Face’s official [dataset card guide](https://huggingface.co/docs/datasets/dataset_card) for the latest template and requirements.
- Some platforms expect YAML front-matter (the `---` block at the top) for metadata; include it if in doubt.
- Include sample rows or data as code blocks to help users understand the format.
- Keep descriptions clear, factual, and concise.

---

**Next Steps**

- Place the `README.md` in your dataset folder.
- If uploading to Hugging Face, the UI and CLI will automatically use and render this card.
- Update sections as your dataset evolves.

Let me know if you need a ready-to-fill template or help generating a card for your specific dataset!


What format should I use to upload a dataset for maximum compatibility?


For **maximum compatibility** when uploading a dataset, you should use **CSV (Comma-Separated Values)** format. CSV is universally supported across most data analysis tools, machine learning platforms, and dashboard applications, including custom and open-source tools.

**Key points for a compatible CSV:**
- Include a header row with column names.
- Use UTF-8 encoding to support all characters.
- Separate values by commas (not semicolons or tabs).
- For fields containing commas, quotes, or newlines, enclose the field in double quotes.
- Use a consistent newline character (`\n`).
- Avoid Excel-specific features like formulas or merged cells.

**Why CSV?**
- It’s lightweight, plain-text, and easily previewed.
- Supported by pandas, Excel, Google Sheets, Next.js APIs, and most dashboard or ML pipelines.

**Additional Notes**
- If your data includes non-ASCII text or complex nested structures, consider JSON, but CSV remains the best first choice unless you have a specific tool with other requirements.
- Always check if the tool you’re uploading to lists stricter requirements (e.g., certain column order or file size limits).

If you have a specific target system or tool in mind, let me know and I can check for any additional requirements!


How do I set up automated dataset updates from a data pipeline?


To set up automated dataset updates from a data pipeline, you generally need a process that takes new data from your pipeline and updates your dataset (whether local, cloud, or through a platform like Hugging Face, DVC, etc.). Below are the **common approaches and recommended steps**—let me know if you want specifics for your stack!

---

**Common Approaches**

- **Scheduled Jobs (CRON, Airflow, Prefect):** Schedule scripts to pull, preprocess, and upload new data at intervals.
- **CI/CD Automation:** Trigger dataset updates through CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Azure Pipelines).
- **Platform Hooks (e.g., Hugging Face, DVC):** Use dataset versioning tools that offer automation hooks for syncing new data.

---

**Typical Steps**

1. **Export New Data**
   - Ensure your pipeline outputs processed data in a consistent location/format (CSV, Parquet, JSON, etc.).

2. **Script the Update**
   - Write a script to pick up the new data, validate it, and update the target dataset store (e.g., regenerate dataset files, push to cloud, or update a hosted service).

3. **Automate the Process**
   - Use your orchestration tool (CRON, Airflow, Prefect, GitHub Actions) to run the update script automatically (e.g., nightly, on new data arrival, or after pipeline success).

4. **(Optional) Dataset Versioning**
   - Incorporate dataset versioning tools (e.g., DVC, Hugging Face Hub Datasets, LakeFS) to track changes and allow rollbacks.

5. **Monitoring & Alerting**
   - Add notifications/logging to alert you if the update fails.

---

**Example: Hugging Face Dataset Automation**

If you're pushing updates to a Hugging Face Dataset repo:

- Script with `datasets` library or CLI to update/reload files.
- Configure a GitHub Action to run on a schedule or webhook, which:
  - Pulls new data from your pipeline/output location.
  - Updates dataset files.
  - Commits and pushes to the HF dataset repo.

---

**Let Me Know!**

- What tooling/orchestration are you using? (Airflow, GitHub Actions, plain SSH/CRON, etc.)
- Where should the dataset live? (Local FS, cloud bucket, data platform, Hugging Face Hub, etc.)
- Is versioning or provenance tracking needed?

If you give me your stack or context, I can write a quick template or script for your case!


How do I benchmark my fine-tuned model against standard evaluations?


To benchmark your fine-tuned model against standard evaluations, you generally want to **evaluate its performance on common (public) benchmark datasets** and compare your results to published or baseline models. Here’s a concise step-by-step guide for most machine learning workflows:

---

**1. Choose Standard Benchmark(s)**
- Select datasets and tasks relevant to your domain (e.g., GLUE for NLP, ImageNet/CIFAR for vision, SQuAD for QA, etc.).
- Make sure the datasets are popular and have published baseline scores.

**2. Prepare Your Model and Data**
- Fine-tune your model as usual.
- Ensure your model’s inputs/outputs match the evaluation dataset format.

**3. Run Evaluation Scripts**
- Use existing libraries/tools to evaluate your model (e.g., `transformers`’ built-in `Trainer.evaluate()`, or HuggingFace `evaluate` library).
- For example, with Hugging Face models:
  ```python
  from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
  from datasets import load_dataset

  model = AutoModelForSequenceClassification.from_pretrained("your-finetuned-model")
  tokenizer = AutoTokenizer.from_pretrained("your-finetuned-model")
  dataset = load_dataset('glue', 'sst2', split='validation')
  pipe = pipeline("text-classification", model=model, tokenizer=tokenizer)
  predictions = pipe(dataset["sentence"])
  # Compute accuracy/F1 using standard metrics
  ```

**4. Use Standard Metrics**
- Accuracy, F1, BLEU, ROUGE, etc.—pick what matches your evaluation benchmark.
- Libraries like [HuggingFace Evaluate](https://huggingface.co/docs/evaluate/index) or [Scikit-learn metrics](https://scikit-learn.org/stable/modules/model_evaluation.html) will help.

**5. Compare Against Baselines**
- Check leaderboard sites (e.g., [Papers With Code](https://paperswithcode.com/), datasets’ README, or task papers).
- Compare your scores to “vanilla” or “published” baseline results.

**6. Report Results**
- Clearly state your model, dataset, metric, and scores.
- Optional: Visualize results for further insight.

---

**Short Example: Benchmarking with Hugging Face CLI**
```sh
# Install dependencies
pip install transformers datasets evaluate

# Example: Evaluate against a GLUE task (SST-2)
python run_glue.py \
  --model_name_or_path your-finetuned-model \
  --task_name sst2 \
  --do_eval \
  --max_seq_length 128 \
  --output_dir ./results/
```

---

**Tips**
- Automate and script your evaluations for reproducibility.
- Always use the _exact_ evaluation splits used in literature.
- Peer-reviewed benchmarks offer best comparability.

Would you like a sample script tailored to your framework or model type? If you share more details (task, HF/TF/PyTorch, etc.), I can give you code and dataset pointers!

transformers
from transformers
AutoTokenizer
from transformers import pipeline


What metrics should I use to evaluate an LLM for coding tasks?


Great question! Evaluating Large Language Models (LLMs) for coding tasks requires a mix of **automated metrics**, **benchmark datasets**, and sometimes **human evaluation**. Here are the primary metrics and methods used:

---

**Automated Metrics**

- **Pass@k (or k-shot Pass Rate)**
  Measures whether the correct solution appears in the top k model outputs for a coding prompt. Widely used in benchmarks like HumanEval and MBPP.

- **Exact Match / Strict Accuracy**
  Percentage of model completions that exactly match the reference solution.

- **Unit Test Pass Rate**
  The proportion of generated code snippets that pass provided unit tests. Highly practical for code correctness.

- **Code Similarity Metrics**
  - Levenshtein/Exact String Match: Measures literal similarity.
  - BLEU/ROUGE: Measures n-gram overlap, but less meaningful for code than for natural language.
  - CodeBLEU: A variant of BLEU adapted specifically for code, considering syntax and structure.

- **Edit Distance / Token-level Accuracy**
  Measures how many token-level edits are needed to reach the reference.

---

**Benchmark Datasets**

- **HumanEval** (OpenAI): Short generative Python coding tasks, evaluated by running generated code against hidden tests.
- **MBPP**: Similar to HumanEval, but with additional complexity and test cases.
- **APPS, CodeContests, MultiPL-E**: Cover more languages and diverse coding problems.

---

**Qualitative/Human Metrics**

- **Code Readability**: Are the outputs clean, idiomatic, and maintainable?
- **Security/Vulnerabilities**: Is the code free from obvious bugs and vulnerabilities?
- **Efficiency**: Does code use efficient algorithms and best practices?
- **Error Type/Explanation**: Can the LLM explain mistakes or errors, not just generate code?

---

**Task-Specific Metrics**

- **Bug Fixing Success**: Ability to fix existing code given a bug report.
- **Code Completion Rate**: Given a partial file, ability to sensibly complete it.
- **Documentation/Comment Quality**: Quality of explanations or docstrings generated.

---

**When Comparing LLMs, Consider:**

- **Dataset Diversity**: Test performance on real-world, adversarial, or previously unseen problems.
- **Robustness**: How models handle edge cases, bad input, or incomplete prompts.
- **Inference Speed & Token Cost**: Especially important for practical deployment.

---

**Summary Table**

| Metric                | What It Measures              | Typical Use            |
|-----------------------|------------------------------|------------------------|
| Pass@1 / Pass@k       | Best-of-k correctness        | Model/human eval       |
| Unit Test Pass Rate   | Code actually works?         | Automated benchmarks   |
| CodeBLEU              | Code similarity (structure)  | Academic/Large sets    |
| Human Eval (Readability, Security) | Practical fitness | Quality audits         |

---

**Best Practice:**
Use a combination: automated (pass@k, unit test rate), benchmark datasets (HumanEval, MBPP), and a small amount of targeted human review for code quality and edge cases.

If you need details on tooling or implementing these metrics, let me know!


Best open-source eval framework for comparing multiple models


Here are the best open-source evaluation frameworks for comparing multiple machine learning (especially LLM or generative model) outputs, as of 2024:

**1. [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)**
- **Focus:** LLMs (text, code, multilingual, etc.)
- **Strengths:** Huge set of standardized benchmarks. Easy to test multiple models (local, API, HuggingFace, OpenAI, etc.). Great for head-to-head comparisons.
- **Best for:** Academic-quality LLM comparison across many tasks.

**2. [HELM (Holistic Evaluation of Language Models)](https://github.com/stanford-crfm/helm)**
- **Focus:** Holistic LLM evaluation (accuracy, bias, cost, toxicity, etc.)
- **Strengths:** Multi-metric, multi-model, reproducible framework. Strong focus on responsible AI and societal impact.
- **Best for:** In-depth, report-style model benchmarking across many axes (not just accuracy).

**3. [OpenAI Evals](https://github.com/openai/evals)**
- **Focus:** Customizable evals (including user-defined tasks/prompts)
- **Strengths:** Plug-and-play for custom eval sets. Active support for LLMs with clear metrics.
- **Best for:** Teams using OpenAI models wanting to run custom benchmark suites.

**4. [Ragas](https://github.com/explodinggradients/ragas)**
- **Focus:** RAG (Retrieval-Augmented Generation) evaluation
- **Strengths:** Advanced metrics for retrieval + generation (faithfulness, answer relevance, context recall, etc.)
- **Best for:** Comparing RAG pipelines, not plain LLMs.

**5. [TextEval](https://github.com/radar-lab/TextEval)**
- **Focus:** Flexible text/LLM eval for few-shot/classification/generation
- **Strengths:** Intuitive interface for building + comparing metrics across models.

---

**How to choose:**

- If you want broad LLM/task evaluation: **lm-eval-harness** or **HELM**.
- If your focus is safe/holistic evaluation: **HELM**.
- For custom tasks/benchmarks (especially API): **OpenAI evals**.
- For comparing search/RAG pipelines: **Ragas**.

**Quick example:**
For most direct comparisons (e.g., comparing GPT-4 to Llama 3 and Mixtral on standard benchmarks), **[lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness)** is the most widely adopted, open, and community-maintained option.

Let me know your use case (LLMs, vision models, RAG, etc.) or tech stack for a more tailored suggestion!


How do I run MMLU and HumanEval benchmarks on a custom model?


Here’s how you can run the MMLU and HumanEval benchmarks on a custom model:

---

**MMLU Benchmark**

- **What is it:** Massive Multitask Language Understanding (MMLU) is a benchmark covering 57 tasks for evaluating language model proficiency.
- **How to run on a custom model:**
  1. **Install repo:**
     The most common framework is from [this repo](https://github.com/hendrycks/test) (official MMLU).
     ```bash
     git clone https://github.com/hendrycks/test.git
     cd test
     pip install -r requirements.txt
     ```
  2. **Prepare your model:**
     Ensure your model is accessible, e.g. via the Hugging Face Transformers API or a custom API endpoint.
  3. **Run evaluation:**
     - Modify `main.py` or create a custom script to load your model via its API.
     - Replace the reference model inference code with your custom inference.
     - For Hugging Face models, see [OpenAI’s sample scripts](https://github.com/openai/grade-school-math/blob/master/evaluate.py) or the [EleutherAI lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness) for wrapper templates.

  4. **Alternative (Recommended):**
     Use [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness) (“Language Model Evaluation Harness”):
     ```bash
     git clone https://github.com/EleutherAI/lm-evaluation-harness.git
     cd lm-evaluation-harness
     pip install -e .
     ```
     Then run (replace `model_args` and `model_name` appropriately):
     ```bash
     python main.py \
         --model hf \
         --model_args pretrained=path/or/model_name \
         --tasks mmlu \
         --device cuda:0
     ```

---

**HumanEval Benchmark**

- **What is it:** Evaluates functional code generation by language models, used in OpenAI's Codex and GPT-4 publications.
- **How to run on a custom model:**
  1. **Clone the repo:**
     ```bash
     git clone https://github.com/openai/human-eval.git
     cd human-eval
     pip install -r requirements.txt
     ```
  2. **Format generations:**
     - Modify or create a script to sample code from your model for each HumanEval prompt.
     - Write generations as one file per completion, following HumanEval’s format.
     - For Hugging Face, see [lm-eval-harness's HumanEval support](https://github.com/EleutherAI/lm-evaluation-harness/blob/master/lm_eval/tasks/humaneval.py).

  3. **Evaluate:**
     - If using HumanEval’s harness:
       ```bash
       python evaluate_functional_correctness.py --samples <your_completions.jsonl>
       ```
     - Or use lm-eval-harness:
       ```bash
       python main.py \
         --model hf \
         --model_args pretrained=path/or/model_name \
         --tasks humaneval \
         --device cuda:0
       ```

---

**References**
- [lm-evaluation-harness Getting Started](https://github.com/EleutherAI/lm-evaluation-harness/tree/main#quick-start)
- [Official HumanEval repo](https://github.com/openai/human-eval)

---

**Tips:**
- `lm-evaluation-harness` is the best-maintained tool, supporting both MMLU and HumanEval with many custom model backends (Hugging Face, vLLM, OpenAI API, DeepSpeed, etc.).
- To use a non-Hugging Face model, implement a custom `--model` backend or use the API wrapper shell.
- For repository-specific details, always read project docs—file formats and API calls may evolve.

Let me know if you want sample code for a specific backend or help with any of the above steps!


How do I set up automated evaluation pipelines for model development?


To set up automated evaluation pipelines for model development, you typically want a system that can automatically test your models against benchmarks or custom tests whenever new code or model changes are made. Here’s a concise overview for modern ML workflows:

---

**1. Choose or Build an Evaluation Framework**

- Use established tools/libraries when possible. Popular choices include:
  - **Hugging Face Evaluate** (`evaluate`), `datasets`, or `transformers` for NLP.
  - **MLflow**, **Weights & Biases** for experiment tracking and evaluation logging.
  - **Custom scripts** with pytest, or Jupyter notebooks for lightweight ad hoc setups.

---

**2. Integrate with CI/CD for Automation**

- Use tools like **GitHub Actions**, **GitLab CI**, or **Jenkins**.
- Typical workflow:
  - On PR or push, spin up environment.
  - Pull latest code and (optionally) checkpointed model(s).
  - Run evaluation suite (compare to gold labels, measure metrics).
  - Upload results/artifacts to tracking system (dashboard, database, or notifications).

---

**3. Steps to Implement:**

- **a. Define Evaluation Scripts**
  - Create scripts (Python recommended) to load model(s), run predictions on test/validation data, compute metrics, and output result files (JSON, CSV, DB, or dashboard entries).
  - Make scripts modular, accepting parameters for model path, dataset, and output location.

- **b. Set Up Data & Model Versioning**
  - Use version-controlled datasets (DVC, Hugging Face Datasets, or plain storage references).
  - Store and reference model artifacts with unique version tags/checkpoints.

- **c. Add CI/CD Configuration**
  - Example: In `.github/workflows/evaluate.yml`:
    ```yaml
    jobs:
      evaluate:
        runs-on: ubuntu-latest
        steps:
          - uses: actions/checkout@v3
          - uses: actions/setup-python@v4
            with: { python-version: '3.10' }
          - run: pip install -r requirements.txt
          - run: python scripts/evaluate.py --model ./models/model.pt --data ./data/test.csv --out results.json
          - run: python scripts/upload_results.py results.json
    ```
  - Adapt for GitLab, Jenkins, or others as needed.

---

**4. Monitor and Compare Results**

- Push evaluation metrics to a dashboard (Weights & Biases, MLflow, or custom web view).
- Use artifact comparisons to display regressions, improvements, or trend lines over time.

---

**5. Optional: Notification & Validation**

- Add steps to fail CI/CD pipelines if metrics drop below thresholds.
- Send summaries via Slack, email, or GitHub comments.

---

**References & Extra Reading**
- [Hugging Face Evaluate Docs](https://huggingface.co/docs/evaluate)
- [MLflow Model Evaluation](https://mlflow.org/docs/latest/model-evaluation/index.html)
- [GitHub Actions for ML](https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions)

---

Let me know your preferred tools, frameworks, or what you’re already using, and I can walk you through a tailored setup!


How do I download model weights in safetensors format?


To **download model weights in `safetensors` format**, the typical method depends on where the model is hosted. Most often, users fetch models from [Hugging Face Hub](https://huggingface.co/). Here’s how you can do it:

---

**From the Hugging Face Hub (Python CLI/Library)**

1. **Install the Hugging Face `huggingface_hub` and `safetensors` libraries:**
   ```bash
   pip install huggingface_hub safetensors
   ```

2. **Download with Python (recommended for custom scripts):**
   ```python
   from huggingface_hub import hf_hub_download

   model_id = "bert-base-uncased"  # change to your model
   weight_file = hf_hub_download(repo_id=model_id,
                                filename="pytorch_model.safetensors")
   print(f"Downloaded to: {weight_file}")
   ```
   - This downloads `pytorch_model.safetensors` if available.
   - Change `filename` as needed (use the file shown on the model’s Hugging Face page).

3. **Download with the CLI:**
   ```bash
   huggingface-cli download bert-base-uncased pytorch_model.safetensors
   ```
   - Replace `bert-base-uncased` and filename as needed.

---

**Common filename patterns**:
- `pytorch_model.safetensors` (PyTorch models)
- `model.safetensors` or similar, depending on repo

**If the model only provides `.bin` files:**
- Not all repos offer `safetensors`. If you see only `.bin` files, ask the model author for a `.safetensors` format, or convert yourself if needed.

---

**Conversion (`.bin` → `.safetensors`):**
If you need to convert, use the [safetensors](https://github.com/huggingface/safetensors) Python library, but note that care must be taken for compatibility.

**Example:**
```python
from safetensors.torch import save_file
import torch

state_dict = torch.load("pytorch_model.bin", map_location="cpu")
save_file(state_dict, "pytorch_model.safetensors")
```
---

**Summary**
- Prefer downloading directly from Hugging Face Hub when `.safetensors` is available.
- Use `hf_hub_download` (Python) or `huggingface-cli download` (CLI).
- Convert using `safetensors` library if needed.

If you tell me the exact model/repo you want, I can give you a direct download command!

safetensors
from safetensors
huggingface/safetensors
.safetensors


Where can I find pre-trained models for my specific use case?


To find pre-trained models tailored to your specific use case, you generally have several options:

- **Hugging Face Model Hub**:
  The [Hugging Face Model Hub](https://huggingface.co/models) is the largest collection of pre-trained models for NLP, computer vision, audio, and more. You can search and filter by task (e.g., text classification, object detection), framework (e.g., PyTorch, TensorFlow), language, and license.

- **OpenAI Models**:
  If you are interested in using OpenAI’s models (like GPT-4, Whisper, CLIP, etc.) for tasks such as chat, summarization, or speech recognition, you can find details and endpoints in the [OpenAI API documentation](https://platform.openai.com/docs/models).

- **TensorFlow Hub**:
  [TensorFlow Hub](https://tfhub.dev/) provides a wide set of pre-trained models for TensorFlow-based projects, suitable for a variety of domains.

- **PyTorch Hub**:
  [PyTorch Hub](https://pytorch.org/hub/) lists pre-trained models contributed by the PyTorch community.

- **Repositories & Model Zoos**:
  - Official “model zoo” sections of libraries like [Detectron2](https://github.com/facebookresearch/detectron2/blob/main/MODEL_ZOO.md) for vision tasks.
  - GitHub repos for specific research projects or organizations (e.g., DeepMind, Google Research).

**How to Choose the Right Model**
- **Identify your task and domain**: (e.g., sentiment analysis for finance, image segmentation for healthcare)
- **Search with relevant keywords** on the above platforms.
- **Review metrics, example outputs, and intended use cases** listed in the model cards.

If you share more about your domain and goal (e.g., “text classification for legal documents” or “object detection for satellite imagery”), I can recommend specific models or places to search. Let me know more details and I can tailor the advice!


How do I search for models by license type?


To search for models by license type, you’ll need to use a model repository or catalog (such as Hugging Face Hub, Open Model Zoo, or others) that provides license metadata with its search and filtering features.

Here’s how to do it using the Hugging Face Hub (the most popular option for open-source ML models):

---

**Hugging Face Hub – Search by License**

1. **Web Interface:**
   - Go to [Hugging Face Hub](https://huggingface.co/models).
   - In the left sidebar, under the "Other" section, find **“License”**.
   - You can filter by a variety of licenses (e.g., mit, apache-2.0, gpl-3.0, etc.).
   - Select your license(s) of interest; results update automatically.
   - Direct URL example for Apache 2.0:
     [https://huggingface.co/models?license=apache-2.0](https://huggingface.co/models?license=apache-2.0)

2. **Using the CLI:**
   ```bash
   # List all models with Apache 2.0 license
   huggingface-cli models list --filter license:apache-2.0
   ```

3. **Using the Python API:**
   ```python
   from huggingface_hub import list_models

   # List models with the 'mit' license
   models = list(list_models(filter="license:mit"))
   for model in models:
       print(model.modelId, model.license)
   ```
   - Replace `"license:mit"` with any supported license tag.

---

**Other Repositories**

- **Open Model Zoo** and similar sites may have search or filter features — look for a license/type filter in their UI or documentation.

---

**Key Points**

- Not all repositories provide license search; Hugging Face does.
- License options may include: `mit`, `apache-2.0`, `gpl-3.0`, `cc-by-4.0`, `openrail`, etc.
- Always double-check the license details before use.

Let me know if you want code or a command for a different platform!


Best way to compare model performance before downloading


To compare machine learning model performance **before downloading**, you typically rely on the following approaches:

**1. Review Public Leaderboards and Benchmarks**
- Many platforms (like Hugging Face, Papers with Code, or OpenML) aggregate benchmark results for popular datasets.
- Leaderboards give metrics such as accuracy, F1, BLEU, perplexity—depending on task (e.g., NLP, vision).

**2. Examine Model Cards and Documentation**
- Most reputable model repositories include a “model card” or README outlining:
  - Evaluation metrics on standard datasets.
  - Intended use-cases and known caveats.
  - Dataset splits, experiment configuration, sometimes even error analysis.

**3. Use Hosted Inference or Preview APIs**
- Hugging Face (and similar platforms) let you run sample inputs against models in-browser or via API.
- Compare outputs qualitatively—try the same prompt or input on different models.

**4. Look for Third-Party Reviews and Benchmarks**
- Researchers and independent engineers often publish blog posts or Github repos that:
  - Reproduce official benchmarks.
  - Compare models across diverse or custom datasets.

**Summary Table of Options**

| Method                     | Quantitative? | Qualitative? | Source                   |
|----------------------------|:------------:|:------------:|--------------------------|
| Public leaderboards        | ✅           | ❌           | Hugging Face, OpenML     |
| Model cards & docs         | ✅           | ❌           | Model README/model card  |
| Hosted inference           | ❌           | ✅           | Hugging Face “Try” tab   |
| Third-party reviews/posts  | ✅           | ✅           | Blogs, GitHub repos      |

**Tips:**
- Always confirm that evaluations use data/task close to your target use case.
- Beware of cherry-picked results—cross-check multiple sources.
- If possible, use the preview/inference API to check model output on your specific task.

If you share the task or models of interest, I can provide more tailored sources or comparisons!


How do I download a quantized version of a large model?


To download a quantized version of a large model from sources like Hugging Face, you typically have two main approaches:

---

**1. Pre-Quantized Models (Easiest)**

Some authors **publish quantized model variants** (e.g., GGML, GPTQ, AWQ, QLoRA, etc.) directly to Hugging Face.
- Look for model cards/README that mention “quantized”, e.g., “TheBloke/Llama-2-70B-GGML” or “mistralai/Mistral-7B-Instruct-v0.2-GPTQ”.
- Use `transformers`, `autoawq`, `llama.cpp`, or other libraries depending on the quantization format.

**Example:**
```bash
# Download a GGML quantized model using huggingface_hub CLI
pip install huggingface_hub
huggingface-cli download TheBloke/Llama-2-70B-GGML --include "*" --local-dir ./llama-2-70b-ggml
```
Or use `transformers`:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-70B-GGML")
tokenizer = AutoTokenizer.from_pretrained("TheBloke/Llama-2-70B-GGML")
```
Check the model's README for instructions—some quantized formats need special loaders!

---

**2. Quantize It Yourself**

If a pre-quantized model doesn’t exist, you can **download the standard model** and quantize it using tools like:
- `bitsandbytes` (for 8-bit/4-bit, integrated with `transformers`)
- `autoawq`, `llama.cpp`, `gptq`, etc. for other quantization methods

**Example (BitsAndBytes/Transformers 8-bit):**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
    "huggingface/large-model-name",
    quantization_config=bnb_config
)
tokenizer = AutoTokenizer.from_pretrained("huggingface/large-model-name")
```
Or quantize offline using `autoawq`/`llama.cpp` and download the quantized weights.

---

**Tips:**
- Always check the model card for recommended quantized versions and usage details.
- Some UIs (Ollama, oobabooga/text-generation-webui) offer "download quantized" options for supported models.
- Quantized files are generally much smaller and faster, but can lose a bit of accuracy.

If you have a **specific model or quantization format** in mind, let me know and I’ll provide exact commands!


How do I upload a fine-tuned model for others to use?