--- base_model: meta-llama/Meta-Llama-3-8B tags: - llama-3 - llama - Mixtral - instruct - finetune - chatml - DPO - RLHF - gpt4 - distillation model-index: - name: OpenBioLLM-8B results: [] license: llama3 language: - en widget: - example_title: OpenBioLLM-8B messages: - role: system content: >- You are an expert and experienced from the healthcare and biomedical domain with extensive medical knowledge and practical experience. - role: user content: How long does it take for newborn jaundice to go away? output: text: >- Newborn jaundice, also known as neonatal jaundice, is a common condition in newborns where the yellowing of the skin and eyes occurs due to an elevated level of bilirubin in the blood. Bilirubin is a yellow pigment that forms when red blood cells break down. In most cases, newborn jaundice resolves on its own without any specific treatment. The duration of newborn jaundice can vary depending on several factors such as the underlying cause, gestational age at birth, and individual variations in bilirubin metabolism. Here are some general guidelines: 1. Physiological jaundice: This is the most common type of newborn jaundice and usually appears within 24-72 hours after birth. It tends to peak between the second and fifth day of life and gradually improves over the next week or two. By the time the baby is one week old, the jaundice should have mostly resolved. 2. Breast milk jaundice: This type of jaundice occurs in breastfed babies and may appear later than physiological jaundice, typically between the fifth and fourteenth day of life. It tends to persist for a longer duration but usually resolves within six weeks after birth. 3. Pathological jaundice: This type of jaundice is less common and occurs due to an underlying medical condition that affects bilirubin metabolism or liver function. The duration of pathological jaundice depends on the specific cause and may require treatment. It's important for parents to monitor their newborn's jaundice closely and seek medical advice if the jaundice progresses rapidly, becomes severe, or is accompanied by other symptoms such as poor feeding, lethargy, or excessive sleepiness. In these cases, further evaluation and management may be necessary. Remember that each baby is unique, and the timing of jaundice resolution can vary. If you have concerns about your newborn's jaundice, it's always best to consult with a healthcare professional for personalized advice and guidance. quantized_by: bartowski pipeline_tag: text-generation --- ## Llamacpp imatrix Quantizations of OpenBioLLM-Llama3-8B Using llama.cpp release b2717 for quantization. Original model: https://huggingface.co/aaditya/OpenBioLLM-Llama3-8B All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) ## Prompt format No chat template specified so default is used. This may be incorrect, check original model card for details. ``` <|im_start|>system {system_prompt}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant ``` ## Download a file (not the whole branch) from below: | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [OpenBioLLM-Llama3-8B-Q8_0.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. | | [OpenBioLLM-Llama3-8B-Q6_K.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. | | [OpenBioLLM-Llama3-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. | | [OpenBioLLM-Llama3-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. | | [OpenBioLLM-Llama3-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. | | [OpenBioLLM-Llama3-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. | | [OpenBioLLM-Llama3-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. | | [OpenBioLLM-Llama3-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | | [OpenBioLLM-Llama3-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. | | [OpenBioLLM-Llama3-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. | | [OpenBioLLM-Llama3-8B-IQ3_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. | | [OpenBioLLM-Llama3-8B-IQ3_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. | | [OpenBioLLM-Llama3-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. | | [OpenBioLLM-Llama3-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. | | [OpenBioLLM-Llama3-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. | | [OpenBioLLM-Llama3-8B-Q2_K.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. | | [OpenBioLLM-Llama3-8B-IQ2_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. | | [OpenBioLLM-Llama3-8B-IQ2_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. | | [OpenBioLLM-Llama3-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. | | [OpenBioLLM-Llama3-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. | | [OpenBioLLM-Llama3-8B-IQ1_M.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. | | [OpenBioLLM-Llama3-8B-IQ1_S.gguf](https://huggingface.co/bartowski/OpenBioLLM-Llama3-8B-GGUF/blob/main/OpenBioLLM-Llama3-8B-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. | ## Which file should I choose? A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. If you want to get more into the weeds, you can check out this extremely useful feature chart: [llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski