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The model was trained on GitHub code as well as additional selected data sources such as Arxiv and Wikipedia. As such it is not an instruction model and commands like "Write a function that computes the square root." do not work well. Here are some examples to get started with the model. You can find a script for fine-tuning in StarCoder2's GitHub repository. First, make sure to install transformers from source: The pretraining dataset of the model was filtered for permissive licenses and code with no license only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a search index that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code. The model has been trained on source code from 600+ programming languages. The predominant language in source is English although other languages are also present. As such the model is capable to generate code snippets provided some context but the generated code is not guaranteed to work as intended. It can be inefficient, contain bugs or exploits. See the paper for an in-depth discussion of the model limitations. The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.
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llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "gg
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \ --custom-api-key "llama.cpp" \ --secret-input-mode plaintext \ --custom-compatibility openai \ --accept-risk
View originalllama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "gg
llama-server -hf ggml-org/gemma-4-26b-a4b-it-GGUF:Q4_K_M openclaw onboard --non-interactive \ --auth-choice custom-api-key \ --custom-base-url "http://127.0.0.1:8080/v1" \ --custom-model-id "ggml-org-gemma-4-26b-a4b-gguf" \ --custom-api-key "llama.cpp" \ --secret-input-mode plaintext \ --custom-compatibility openai \ --accept-risk
View original@LottoLabs https://t.co/h2frA6iR2I
@LottoLabs https://t.co/h2frA6iR2I
View originalLet's go! https://t.co/HakmkNzDT2
Let's go! https://t.co/HakmkNzDT2
View originalModel weights are here: https://t.co/rQlfP51Db7!
Model weights are here: https://t.co/rQlfP51Db7!
View originaldo the right thing anon!
do the right thing anon!
View originalhttps://t.co/QLPgege4CI
https://t.co/QLPgege4CI
View originalSeeing the worldwide demand we are kicking off global applications for Hugging Face Builders! If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️
Seeing the worldwide demand we are kicking off global applications for Hugging Face Builders! If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️ Learn more about the program and apply to become a Builder ➡️ https://t.co/MR0fmruSDi
View originalWe are sponsoring Gemini hackathon with Cerebral Valley, see you this weekend!
We are sponsoring Gemini hackathon with Cerebral Valley, see you this weekend!
View originalLearn more and apply from the link below🤗 https://t.co/QLPgege4CI
Learn more and apply from the link below🤗 https://t.co/QLPgege4CI
View originalHugging Face Builders is a global community program that puts local leaders at the center of the open-source AI movement 🤗 If you're passionate about open AI and love bringing people together, this
Hugging Face Builders is a global community program that puts local leaders at the center of the open-source AI movement 🤗 If you're passionate about open AI and love bringing people together, this is your invitation to lead ✉️ Apply for to build the Paris chapter today ➡️ https://t.co/ONVBZdxRdc
View originalRead our blog to learn more 🤗 https://t.co/asj0iZulGe
Read our blog to learn more 🤗 https://t.co/asj0iZulGe
View original🪣 We just shipped Storage Buckets: S3-like mutable storage, cheaper & faster Git falls short for everything on high-throughput side of AI (checkpoints, processed data, agent traces, logs etc) Buc
🪣 We just shipped Storage Buckets: S3-like mutable storage, cheaper & faster Git falls short for everything on high-throughput side of AI (checkpoints, processed data, agent traces, logs etc) Buckets fixes that: fast writes, overwrites, directory sync 💨 All powered by Xet dedup so successive checkpoints skip the bytes that already exist ➡️
View original@gokayfem thank you for the all the open sourcing 🤗 https://t.co/hhmff7iy2g
@gokayfem thank you for the all the open sourcing 🤗 https://t.co/hhmff7iy2g
View originalRepository Audit Available
Deep analysis of bigcode-project/starcoder2 — architecture, costs, security, dependencies & more
StarCoder uses a tiered pricing model. Visit their website for current pricing details.
Key features include: bigcode/the-stack-v2-train-full-ids, 💫 StarCoder2, StarCoder 2 and The Stack v2: The Next Generation, Efficient Training of Language Models to Fill in the Middle, FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness, Longformer: The Long-Document Transformer.
StarCoder has a public GitHub repository with 2,050 stars.
Based on 35 social mentions analyzed, 0% of sentiment is positive, 100% neutral, and 0% negative.
