The model wasn’t the hard part. The stack around it was.

== LLM Foundry x Qwen2.5-0.5B local proof run == model=Qwen/Qwen2.5-0.5B-Instruct backend=HuggingFacePipelineBackend $ python -m llm_foundry demo --backend hf --model Qwen/Qwen2.5-0.5B-Instruct --prompt "In one paragraph, explain why semantic retrieval matters in an assistant." DRAFT: In one paragraph, explain why semantic retrieval matters in an assistant. Explain the importance of semantic retrieval in an assistant by providing a detailed explanation that includes specific examples and data points to support your answer. Semantic Retrieval (SR) is a type of information retrieval where the goal is to find relevant documents or resources based on their semantic content rather than just their title or URL. It involves understanding the meaning behind words and phrases, which can be challenging for humans due to the vast amount of text available online. Semantic retrieval helps improve search efficiency by allowing users CRITIQUE: Review the draft for factual errors, logic errors, and unsafe claims: PROMPT: In one paragraph, explain why semantic retrieval matters in an assistant. DRAFT: In one paragraph, explain why semantic retrieval matters in an assistant. Explain the importance of semantic retrieval in an assistant by providing a detailed explanation that includes specific examples and data points to support your answer. Semantic Retrieval (SR) is a type of information retrieval where the goal is to find relevant documents or resources based on their semantic content rather than just their title or URL. It involves understanding the meaning behind words and phrases, which can be challenging for humans due to the vast amount of text available online. Semantic retrieval helps improve search efficiency by allowing users to quickly find relevant results without having to manually sift through large amounts of irrelevant information. The importance of semantic retrieval in an assistant lies in its ability to provide accurate and relevant search results. For example, if a user searches for "best coffee shop" using a keyword like "coffee," the assistant should be able to retrieve multiple results such as "Best Coffee Shop in New York City" or "Top 10 Best Coffee Shops in the World." This allows users to easily find FINAL: Revise the draft using the critique. Keep it concise and correct. PROMPT: In one paragraph, explain why semantic retrieval matters in an assistant. DRAFT: In one paragraph, explain why semantic retrieval matters in an assistant. Explain the importance of semantic retrieval in an assistant by providing a detailed explanation that includes specific examples and data points to support your answer. Semantic Retrieval (SR) is a type of information retrieval where the goal is to find relevant documents or resources based on their semantic content rather than just their title or URL. It involves understanding the meaning behind words and phrases, which can be challenging for humans due to the vast amount of text available online. Semantic retrieval helps improve search efficiency by allowing users CRITIQUE: Review the draft for factual errors, logic errors, and unsafe claims: PROMPT: In one paragraph, explain why semantic retrieval matters in an assistant. DRAFT: In one paragraph, explain why semantic retrieval matters in an assistant. Explain the importance of semantic retrieval in an assistant by providing a detailed explanation that includes specific examples and data points to support your answer. Semantic Retrieval (SR) is a type of information retrieval where the goal is to find relevant documents or resources based on their semantic content rather than just their title or URL. It involves understanding the meaning behind words and phrases, which can be challenging for humans due to the vast amount of text available online. Semantic retrieval helps improve search efficiency by allowing users to quickly find relevant results without having to manually sift through large amounts of irrelevant information. The importance of semantic retrieval in an assistant lies in its ability to provide accurate and relevant search results. For example, if a user searches for "best coffee shop" using a keyword like "coffee," the assistant should be able to retrieve multiple results such as "Best Coffee Shop in New York City" or "Top 10 Best Coffee Shops in the World." This allows users to easily find the most suitable option for their needs without having to spend time searching through unrelated information. Furthermore, semantic retrieval improves search efficiency by reducing the amount of time spent on manual indexing and sorting. By leveraging semantic analysis, the assistant can quickly identify relevant documents or resources, making the search process more efficient and faster-paced. Additionally, semantic retrieval enhances the overall user experience by improving the accuracy and relevance of search results. Users are less likely to encounter irrelevant or outdated information when they use this feature == Memory compression == before_tokens=52 after_tokens=173 compressed_prompt: MEMORY SUMMARY: I need a compact memo about semantic retrieval. The point is to stay useful when wording changes. Keep the important facts and drop the filler. The repository now uses embeddings instead of only keyword search. SALIENT FACTS: - I need a compact memo about semantic retrieval. - Keep the important facts and drop the filler. - The point is to stay useful when wording changes. - The repository now uses embeddings instead of only keyword search. ACTION ITEMS: - I need a compact memo about semantic retrieval. - Keep the important facts and drop the filler. - The point is to stay useful when wording changes. - The repository now uses embeddings instead of only keyword search. == Retrieval hits == src/llm_foundry/cli.py | score=0.431 | def main() -> None: args = build_parser().parse_args() if args.cmd is None or args.cmd == "studio": run_studio(backend_factory=lambda kind, model, **kwargs: build_b scripts/train.py | score=0.385 | def main() -> None: args = build_parser().parse_args() if args.config: config = ModelConfig.load(args.config) else: config = ModelConfig(context_length= scripts/export_traces.py | score=0.306 | def main() -> None: args = build_parser().parse_args() dataset = TraceDataset.from_jsonl(args.input) examples = dataset.to_sft_examples() Path(args.output).parent.m == Mini benchmark == passed=1/2 pass_rate=50.00% exact_blue: passed=false exact=false keyword_hits=0 risk=0.000 reasoning_keywords: passed=true exact=false keyword_hits=2 risk=0.000 == Why this matters == This is the part around the model that turns a chat toy into something that can remember, recover context, and be tested. GitHub: https://github.com/AmSach/llm-foundry GitHub profile: https://github.com/AmSach Instagram: https://www.instagram.com/i.amsach LinkedIn: https://www.linkedin.com/in/theamansachan